Skip to main content

Applications of single-cell sequencing in cancer research: progress and perspectives

Abstract

Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics sequencing, is a powerful tool to decipher the cellular and molecular landscape at a single-cell resolution, unlike bulk sequencing, which provides averaged data. The use of single-cell sequencing in cancer research has revolutionized our understanding of the biological characteristics and dynamics within cancer lesions. In this review, we summarize emerging single-cell sequencing technologies and recent cancer research progress obtained by single-cell sequencing, including information related to the landscapes of malignant cells and immune cells, tumor heterogeneity, circulating tumor cells and the underlying mechanisms of tumor biological behaviors. Overall, the prospects of single-cell sequencing in facilitating diagnosis, targeted therapy and prognostic prediction among a spectrum of tumors are bright. In the near future, advances in single-cell sequencing will undoubtedly improve our understanding of the biological characteristics of tumors and highlight potential precise therapeutic targets for patients.

Background

Single-cell sequencing, based on next-generation sequencing, has progressed rapid in recent years. The first single-cell mRNA sequencing experiment was conducted in 2009, the first single-cell DNA sequencing experiment in human cancer cells was conducted in 2011, and the first single-cell exome sequencing experiment was conducted in 2012 [1, 2]. Single-cell sequencing significantly outperforms previous sequencing technologies in terms of our understanding of the human biology of embryonic cells, intracranial neurons, malignant tumor cells and immune cells because it can probe cellular and microenvironmental heterogeneity at single-cell resolution. It has revolutionized our ability to interrogate the transcriptional, genomic, epigenomic and metabolic characteristics of thousands of individual cells in depth, thereby enabling an unbiased analysis of the cells within tumor lesions. It also provides molecular insights, including single-nucleotide variations (SNVs), copy number variations (CNVs) and structural variations (SVs) [3, 4]. However, the limitations of single-cell sequencing should not be neglected; these include its limited sensitivity, scale and accuracy; insufficiency in reconstructing clonal evolution in spatial and temporal order; and noise due to the preamplification of single-cell RNA. These limitations have mainly been overcome by improving the technology itself and combining it with other emerging technologies, enabling researchers to analyze multiomic information at a single-cell resolution.

Single-cell sequencing offers the ultimate resolution, thereby contributing to exploring effective management and personalized therapeutics in a tremendous number of scenarios, such as developmental research, the preparation of human cell atlases and cancer research [5, 6]. In addition to developmental biology, single-cell RNA sequencing (scRNA-seq) and its derived technologies might assist in the identification of novel markers, rare subgroups and evolution patterns, especially in brain development [7, 8]. In cancer research, it can be used to identify or interrogate (1) rare subpopulations; (2) circulating tumor cells (CTCs); (3) the tumor or immune microenvironment; (4) tumor heterogeneity and molecular subtype; (5) mechanisms associated with tumorigenesis, progression, metastasis, evolution, relapse and therapy resistance; and (6) cancer stem cells (CSCs) [9,10,11,12].

In this review, we summarize recent progress in single-cell sequencing technologies, including emerging single-cell sequencing technologies, single-cell sequencing data analysis, and applications in cancer research.

Emerging single-cell sequencing technologies

Breakthroughs in single-cell isolation, sequencing, cDNA library preparation and analytical algorithms have led to rapid progress in single-cell sequencing analysis capacity. Single-cell transcriptomics, genomics, proteomics, epigenomics and interactomics sequencing have developed rapidly in recent years.

scRNA-seq technologies share common procedures, including (1) the isolation of a single cell, (2) RNA extraction, (3) reverse transcription, (4) preamplification and (5) detection [13]. The earliest procedures are clearly the most crucial, as they determine the accuracy and amount of the amplified material. A study showed that a lower single-cell dissociation temperature (6 °C) minimizes the stress responses induced at 37 °C, which includes the induction of 512 heat shock proteins. Moreover, the method of dissociation also affects transcription signatures [14].

In addition to the need for single-cell isolation, the greatest challenge in single-cell sequencing is recognizing the sequencing results at the single-cell level. Single-cell barcoding technologies based on plate microreaction systems and combinational indices have completely resolved this bottleneck, thus increasing the throughput of single-cell analysis by at least 100-fold. First, plate microreaction systems usually include single cells, functional beads and reverse transcriptomes. The surface of the functional beads is modified with oligonucleotides, including primers, cell barcodes, unique molecular identifiers (UMIs) and poly(dT) moieties from 5′ to 3′. The primer and poly(dT) moieties are uniform among the microreaction systems, while the cell barcode is unique for each microreaction system, and the UMI is unique for each molecule within a single cell. In addition, the UMI can label various molecules, such as the DNA genome, transcriptome, immune profile and proteome, based on different sequencing purposes. These features guarantee the precise identification of molecular features of a single cell. Drop-seq, Seq-Well and inDrop are bead-based technologies. Zhang et al. revealed the differences between Drop-seq and inDrop in detail. For example, the length of the cell barcode is 38–41 bp in inDrop and 12 bp in Drop-seq, and the cell barcode capacity is 147,456 (384*384) in inDrop and 16,777,216 (412) in Drop-seq [15]. Additionally, the same type of bead is used in Seq-Well and Drop-seq [16]. Another single-cell barcoding technology based on combinational indexing overcomes the limitations of the relatively high cost of isolating a single cell for bead-based barcoding technologies. Combinational indexing-based barcoding technologies recognize single cells by the addition of cellular barcodes in multiple rounds without isolating single cells; these technologies include Sci-Seq, Microwell-Seq and Split-Seq [17]. In Sci-Seq, two rounds of transposase barcoding and PCR ligation labeled 9216 (96*96) single cells. Three split-pool rounds introduced 3 parts of the oligonucleotide sequence to the magnetic beads in Microwell-Seq [18]. However, too few cells for massive analysis are isolated in Sci-Seq and Microwell-Seq. Split-seq, which is based on Sci-Seq, utilizes 5 rounds of barcoding, which significantly improves sequencing over millions of single cells and lowers the cost [8].

After barcode-targeted molecules, the next important procedure is preamplification, in which the transcripts undergo reverse transcription. The preamplification procedure in 10X Genomics technology occurs as follows. First, the transcripts are bound to the 5′ poly(dT) in oligonucleotides by the polyA tail at its 3′ end. Second, the oligonucleotides undergo reverse transcription from the 5′ end to the 3′ end, with the binding transcript used as a template. The oligonucleotide chain extension ends with several C bases whose addition is catalyzed by a special terminal transferase. Third, the template switch oligo (TSO) is added to the template by reverse transcriptase, followed by the addition of the TSO to the other chain. Then, the new full-length cDNA, which is also known as the first-strand cDNA, becomes the new template for cDNA amplification, called the template switch. Fourth, the second cDNA chain is synthesized with the first cDNA chain used as a template [19]. The whole process is called in vitro transcription. In addition, multiple displacement amplification (MDA) is a non-PCR-type DNA amplification method that relies on isothermal amplification. MDA utilizes a special DNA polymerase named bacteriophage phi29 DNA polymerase, which can amplify very small amounts of DNA even from a single cell with a high binding capacity [20]. In addition, newly developed in situ 10-cell RNA sequencing, which utilizes Taq and Phusion polymerases, has dramatically increased the yield of ~ 500 bp preamplification products and enables a tenfold decrease in the T24-containing primer concentration without a detectable loss in preamplification efficiency. In addition, in situ 10-cell RNA sequencing allows the simultaneous sequencing of 10 microdissected cells in their normal tissue context [21].

For detection, most single-cell sequencing techniques rely on empirical parameters or predefined structures to estimate the degree of complexity. Recent research revealed that the combination of nonnegative matrix factorization and Bayesian model comparison with current algorithms enables unambiguous assessments of the depth of heterogeneity in the tumor microenvironment (TME) [22]. Single-cell hierarchical Poisson factorization can be used to discover both continuous and discrete expression patterns from de novo scRNA-seq data [23]; moreover, newly developed super CT can train the expandable supervised classifier once the RNA data are input [24]. The accurate interrogation of cell subsets contributes to a better understanding of the clonal composition and heterogeneity.

Moreover, we have summarized the most important single-cell sequencing technologies and platforms in Table 1 [25].

Table 1 Summary of important single-cell sequencing technologies and platforms

Transcriptomics

Currently, scRNA-seq is widely applied to profile the transcriptomes of individual cells. Droplet-based 10X Genomics Chromium and plate-based Switching Mechanism at the 5′ End of RNA Template sequencing (SMART-Seq) are two frequently used platforms.

The 10X Genomics platform, which is based on a microfluidics approach, is capable of isolating, labeling, amplifying and preparing a cDNA library from 5000 to 10,000 single cells at a high speed. However, it detects only the 3′ or 5′ end of the transcript with a bias, and abundant cells in a single sample (recommended over 90%) are needed. Moreover, the cell capture rate is lower than that of SMART-Seq, so it is not suitable for detecting rare samples containing few cells.

SMART-Seq, which was reported in 2012, facilitates the detection of full-length transcripts [26]. SMART-Seq v2, which was reported in 2013, eliminates purification procedures, replaces the last guanosine at the TSO 3′ end with locked nucleic acid (LNA) and utilizes betaine to increase protein thermal stability, thus significantly increasing the yield [27]. Interestingly, the fundamental basis for full-length sequencing is Moloney murine leukemia virus (MLV) reverse transcriptase, which prefers to use full-length cDNAs as substrates for its terminal transferase. Moreover, SMART-Seq v4 is more efficient in template switching, which shortens the time of cDNA synthesis and cDNA library construction, and has a higher sensitivity for low input and higher reproducibility [28]. SMART-Seq v2 and v4 are widely utilized in cancer research, such as research on hepatocellular carcinoma (HCC) [29,30,31]. In addition, SMART-Seq does not require additional equipment, which means that it largely depends on experienced researchers.

A recent study compared sequencing data from CD45 cell samples generated by the 10X Genomics and SMART-Seq v2 platforms. In particular, SMART-Seq v2 was more sensitive and detected more genes within a single cell, especially low-abundance transcripts and alternatively spliced transcripts. The use of 10X Genomics increased dropout and showed increased noise in transcripts with low expression. However, 10X Genomics detected more genes due to its superior coverage of abundant cells, which thus enabled the identification of rare cell types. Further analysis of the sequencing data revealed that the two platforms detected distinct differentially expressed genes (DEGs) between cell clusters, indicating the potential to combine complementary results to interrogate heterogeneity [32]. Another study combining 10X Genomics and SMART-Seq v2 revealed the immune cell landscape and analyzed dynamic migration and status switch features in HCC [33].

Spatial transcriptomes and temporal lineage tracing facilitate multifaceted interrogation of the local environment and dynamic interactions within a single cell. Temporal and spatial variability influence tumor heterogeneity and stress reactions and are thus indispensable for cancer diagnosis, subtyping, classification and treatment in cancer research [34,35,36]. The sequential attachment of spatial barcodes makes it possible to encode and obtain location information on single cells, which can effectively provide information for use in research and diagnosis. For example, a recent study deciphered a detailed spatial map of single-cell phenotypes and cellular communities, which demonstrated phenotypic heterogeneity in the breast TME [37]. Furthermore, single-molecule RNA fluorescence in situ hybridization (smFISH) provides precise spatial information in pancreatic cancer and breast cancer [38, 39].

Cancer progression is a dynamic process that involves several different steps from oncogenesis and metastasis to the development of treatment resistance. Defining the temporal and molecular nature of each step in this process is critical to understanding cancer biology and designing effective treatment strategies. The newly developed technique lineage tracing by nuclease-activated editing of ubiquitous sequences (LINNAEUS) was used to reconstruct lineage trees in zebrafish with great success [40]. Single-cell analysis based on CRISPR-Cas9 technology was used to draw a detailed malignant cell spectrum in a KRAS mutant mouse model. This technique overcomes the difficulty of low mutation sensitivity, as well as the inability to decipher the changing details of tumor subtypes, which contributes to tracking the spread pattern and key genes in lung cancer. These novel findings indicated the potential for developing targeted therapy and improving the clinical management of lung cancer in patients with KRAS mutations [41]. Another study in which scRNA-seq and high-confidence clonal tracing were combined demonstrated the first detailed characterization of leukemic stem cells, shedding new light on the understating of leukemia oncogenesis and treatment [42].

Single-nucleus RNA sequencing (snRNA-seq) dates back to 2013, when Grindberg found that it was difficult to dissociate integrated cells from brain tissue [43]. Div-seq and DroNc-seq have been used to resolve the difficulty in identifying rare neural cells and obtaining integrated cell types [44, 45]. At the same time, researchers have conducted snRNA-seq analysis of brain tissues in autopsies to expand the cell landscape retrieved from scRNA-seq data, demonstrating that frozen samples can be analyzed by snRNA-seq [7]. Currently, snRNA-seq has been widely utilized for different tissues and cell types, such as the kidney, heart, lung, pancreas, and, especially, brain tissue [46,47,48,49,50]. Compared with scRNA-seq, scRNA-seq has several advantages. First, snRNA-seq can be utilized to analyze valuable frozen samples, as the nuclear membrane may remain integrated, unlike the cell membrane in frozen tissues [51]. Second, snRNA-seq does not introduce artifactual transcriptional stress responses or transcriptional bias, which may be induced by single-cell isolation in scRNA-seq, thus reflecting the real transcriptional status. Third, snRNA-seq can avoid the loss of specific cell types due to different vulnerabilities to proteases. Fourth, the preparation of a single nucleus is simpler than that of a single-cell suspension, which can minimize the generation of pseudocell populations induced by enzymatic hydrolysis and mechanical pressure.

SnRNA-seq has been widely utilized to analyze brain tumors because it is difficult to obtain fresh brain tissue for research, and the majority of samples are frozen. In 2020, researchers from MIT conducted scRNA-seq and snRNA-seq analyses of fresh or frozen tumor samples and evaluated the sequencing results with respect to cell and nuclear quality, cellular composition and other indicators. The comparison indicated that the two sequencing technologies detected similar cell types with distinct cellular proportions among different tissues. For example, the proportion of immune cells was higher, and the parenchymal cells (e.g., neural crest and neuroendocrine cells) were significantly reduced in neuroblastoma upon analysis by scRNA-seq. However, the proportion of substantial cells (especially malignant cells) was higher, while distinct immune cells were significantly reduced or even absent in the snRNA-seq data. Most importantly, significantly fewer neurons were identified by scRNA-seq than by snRNA-seq, which demonstrates the great potential of snRNA-seq in deciphering the landscape of brain tumors [52]. SnRNA-seq can obtain intron-region and intergene-region sequencing data, enabling cell type identification at a higher resolution and providing relatively richer gene information. However, the amount of RNA in a single nucleus is still significantly lower than that in whole cells, which suggests that snRNA-seq may not be appropriate for immune cell studies.

Genomics

Single-cell DNA sequencing has not yet made the transition to high-dimensional analysis due to its high cost. Therefore, the more economic method is to apply bulk sequencing first, followed by targeted single-cell DNA sequencing of the mutations or variations of interest. For example, TARGET-seq, which integrates genomic DNA and coding DNA genotyping, achieves good coverage across key mutation hotspots and enables the highly sensitive analysis of mutations within single cells. However, TARGET-seq relies on an analysis of known mutations and does not support the identification of new mutations [53]. Additionally, the computational method Cardelino integrates clone tree information from bulk exome sequencing and infrequent variant alleles from scRNA-seq, delineates phenotypic variations between clones and has revealed DEGs between cancerous and healthy skin tissues involved in the cell cycle and proliferation pathways in [54].

Epigenomics

Epigenetic dynamics can also be detected by single-cell sequencing technologies, such as chromatin immunoprecipitation sequencing (ChIP-seq) and assays for transposase-accessible chromatin using sequencing (ATAC-seq) [55]. In applying ATAC-seq to T cell receptor (TCR) research, transcript-indexed ATAC-seq (T-ATAC-seq) was reported to delineate the TCR specificity and epigenomic state of single T cells in cutaneous T cell lymphoma [56]. Furthermore, researchers introduced single-cell Cleavage Under Targets and Tagmentation (CUT&Tag) technology to probe the histone modification landscape in sequences including promoters, enhancers and gene bodies, as well as their dynamic regulatory interactions and single-cell chromatin occupancy, with high sensitivity and throughput within single cells [57].

To understand genome organization, researchers have used in situ genome sequencing (IGS) as a method for simultaneous in situ sequencing and imaging of genomes within intact single cells. IGS, which includes in situ genomics DNA library construction, in situ sequencing, amplicon dissociation, PCR and ex situ sequencing of amplicons and spatially localized sequences, reveals the precise localization of specific DNA sequences. IGS clearly provides a valuable opportunity for addressing intensive biological questions, such as the relationships between genome architecture and diseases [58]. Moreover, another study combined high-resolution multiple annealing and looping-based amplification cycles for digital transcriptomics (MALBAC-DT) and diploid chromatin conformation capture (Dip-C) to delineate transcriptomics dynamics and the three-dimensional genome architecture in single brain cells. This novel method could specifically decipher the roles of transcriptomics and genome architecture, as well as the interactions between function, anatomy, transcription and cell types in tumorigenesis and progression [59].

Proteomics

Proteins represent the main functional machinery of cells, so deciphering the expressed proteome at the single-cell level has attracted great interest. Mass spectrometry is the basis for proteome detection and quantification; however, it is suitable for only the most abundant of proteins. Researchers have improved the procedures used for protein preparation and isolation, thus decreasing protein loss and facilitating more in-depth quantitative proteomics sequencing at a single-cell resolution. Mass cytometry by time of flight (CyTOF), which relies on metal isotope-labeled antibodies conjugated with specific signal molecules on the surface or inside cells for immunolabeling, allows the profiling of 100 distinct proteins in single cells. Imaging mass cytometry (IMC) was developed based on immunohistochemistry with metal-labeled antibodies, and CyTOF. IMC can simultaneously analyze up to 40 protein markers and their spatial architecture and interactions, information that would be lost by traditional tissue lysis to single cells [60]. Importantly, IMC can be performed with paraffin-embedded tissue sections, so it can be applied for retrospective analyses of patient cohorts whose outcomes are known, eventually benefitting personalized medicine. Another single-cell proteomics sequencing technology is liquid chromatography–mass spectrometry (LC–MS), a bioanalytical method for the quantitative analysis of proteins that is widely applied in biopharmaceutical drug development and drug toxicology studies. Recently, single-cell proteomics by mass spectrometry (SCoPE-MS) was introduced to quantify multiplexed single-cell proteomes [61]. As expected, single-cell proteomics sequencing will change the landscape of investigative pathology, particularly when used in coordination with multiomic platforms, such as transcriptomic and proteomic data, at a single-cell resolution.

Multiomics

Progress in sequencing has enabled the integration of several sequencing technologies to delineate the TME and interactome within a single cell. First, simultaneous quantification of the DNA–protein interactome and transcriptome profile in a single cell would help researchers understand the transcriptional changes that occur when DNA binds a protein of interest, and such quantification has been realized by scDam&T-seq [62]. Second, concurrent sequencing of the transcriptome and targeted genomic regions (e.g., CORTAD-seq, G&T-Seq, and DR-Seq) within the same single cell can provide good coverage of the targeted genomic loci crucial for SNVs, deletion mutations and CNVs, which are responsible for resistance to targeted therapy in lung cancer [63]. The DNA methylome and mRNA transcriptome were simultaneously analyzed using scMT-Seq and scM&T-seq. Clone alignment statistically integrates independent single-cell RNA and DNA sequencing data [64]. Third, a recently reported integrative pipeline including 3D imaging based on the rapid clearing agent FUnGI was applied in breast tumors and revealed a significant reduction in tumor clones during oncogenesis, with the luminal progenitor found to be a key cell of origin [65]. LSR-3D imaging, together with multicolor lineage tracing and molecular analysis, provides essential visual and spatial information related to the tumor to study biological processes, highlighting the inherent plasticity of tumors. Fourth, several technologies, such as CITE-seq, REAP-seq and IN-seq, contribute to delineating the transcriptome and proteome at the same time [66].

Analysis of single-cell sequencing data

Single-cell sequencing is widely used to detect DEGs and can detect key signature genes during tumor progression. Several bioinformatics tools are available to mine scRNA-seq data and provide valuable insights. For example, mixed isolated cells could be hierarchically clustered into various subsets based on DEGs along the pseudotime or CNVs, which can be used to construct clonality trees, as observed in medulloblastoma [67]. Second, SCENIC analysis based on gene expression levels could reveal the dynamic interactions between genes within single cells, construct a gene regulatory network and reveal the regulatory changes during phenotype switches. Third, analysis of enriched gene ontology terms and pathway analysis based on Kyoto Encyclopedia of Genes and Genomes could delineate the expression of genes involved in specific signaling pathways associated with tumorigenesis, the cell cycle, epithelial-to-mesenchymal transition (EMT) and immune responses among various subclusters. Notably, when expression information is combined with certain cell types, we can decipher the functions of the cell types during processes in tumor development, such as angiogenesis and tissue remodeling [68].

Applications of single-cell sequencing in cancer biology

The cancer cell atlas and malignant cell heterogeneity

In addition to the Human Cell Atlas, single-cell sequencing technology provides an unprecedented opportunity to decipher the functional states of single cancer cells. The accumulation of increasingly abundant single-cell sequencing data enabled establishment of The Cancer Cell Atlas, which covers a spectrum of cancers (Table 2). For example, Microwell-Seq contributed to cellular hierarchy construction and clonal heterogeneity deciphering in normal bone marrow and acute myeloid leukemia [69]. By integrating clinical pathological information and single-cell sequencing data, novel diagnostic and prognostic biomarkers and potential therapeutically relevant cell types or states could be deciphered [70]. In conclusion, The Cancer Cell Atlas can provide reference information for neoadjuvant therapy, especially for those for which baseline guidance is lacking [71].

Table 2 Establishment of the Cancer Cell Atlas by single-cell sequencing technologies

The TME, which comprises cellular and noncellular components, plays crucial roles in tumorigenesis, progression, invasion, metastasis and drug resistance. These components orchestrate a tumor-promoting and tumor-inhibiting microenvironment that modulates tumor growth and influences tumor evolution, in which heterogeneity is also involved. The molecular basis for heterogeneity manifests as distinct malignant subpopulations based on structural variations, chromosomal rearrangement events, epigenetic modifications and gene expression signatures [72,73,74,75,76,77]. Moreover, nongenetic intratumor heterogeneity is a major predictor of phenotypic heterogeneity and evolutionary dynamics, rather than genomic features alone, in lung cancer [78]. However, heterogeneity constitutes the main obstacle to developing effective therapeutics; thus, deciphering the tumor tissue heterogeneity will substantially contribute to improving our understanding of the underlying mechanisms and developing precise therapies in clinical trials. Interestingly, recent findings showed that heterogeneity is stable over time across replicates of the same culture, strongly suggesting a regulated rather than a stochastic process, as described previously [79].

Compared to traditional bulk RNA sequencing, which reflects the average profiles of gene mutation and expression, scRNA-seq examines the multiomics features of individual cells, thus mapping the TME among various tumors, such as breast cancer [80]. These scRNA-seq data provide supporting evidence for accurate molecular subtyping and precise treatment (Fig. 1).

Fig. 1
figure1

Application of single-cell sequencing in delineating tumor heterogeneity and designing novel targeted therapies for patients with various tumors. a Single-cell sequencing can be used to analyze differentially expressed genes (DEGs), thereby detecting key genes and signaling pathways that are altered during tumor progression and constructing a regulatory network and clonality trees within tumor lesions. When DEGs are combined with canonical markers, the cells are clustered, which enables the identification of rare subpopulations, cell states and phenotype switches during tumor progression. Interrogation of the tumor microenvironment (TME) and heterogeneity enables the disclosure of therapeutic resistance mechanisms and the design of novel therapies. b Single-cell sequencing explores tumor heterogeneity at distinct levels, including the population, individual cell, tissue and molecular levels

Identification of dynamic gene expression profiles during tumor progression

Single-cell sequencing can delineate changes in gene expression during tumor progression. Integrative single-cell sequencing of adjacent normal tissues and adenomas at different stages in patients comprehensively showed genomic alterations, clonal architecture and metabolic dynamics during tumorigenesis, providing insights into the inhibition of tumor progression [81]. For example, an evolutionary trajectory including the carcinogenesis of basal cells and activation of Wnt, followed by differentiation into luminal-like cells, was revealed in salivary gland squamous cell carcinoma [82]. Another example is the transformation of familial adenomatous polyposis to adenocarcinoma. Chen et al. discovered an intermediate epithelial–mesenchymal status, indicating that malignant cells retain epithelial characteristics while undergoing rapid migration in breast cancer [83]. A study of oncogene-induced senescence suggested a new mechanism of the tumor suppressor response to oncogene activation [84]. Interestingly, chromosome remodeling, including interwoven chromosome breakage–fusion–bridge cycles, micronucleation events and chromothripsis episodes, might also drive tumor evolution and cancer-associated thrombosis [85, 86].

Identification of novel subpopulations, cell states and phenotype switches

Individual tumor cells tend to primarily cluster according to their patient origin, which is not surprising because each tumor has a specific evolutionary trajectory with interpatient heterogeneity. Meanwhile, scRNA-seq can cluster cells based on cell type with t-distributed stochastic neighbor embedding (t-SNE), which is an algorithm designed to deconvolve and visualize high-dimensional scRNA-seq data in an unsupervised manner. Principal component analysis (PCA) and canonical correlation analysis order the cells in a gradient to interrogate the features of the clusters. Generally, the cell clusters compromise malignant tumor cells, stromal cells (epithelial cells, endothelial cells, and cancer-associated fibroblasts (CAFs) and immune cells. Further subpopulation clustering contributes to defining rare subpopulations or a spectrum of cellular states of malignant cells. By combining an analysis of signaling pathways, we were able to identify the most malignant subtype associated with a poor prognosis, which may provide insights into targeted therapy for this subtype. Recently, scRNA-seq research identified melanoma cells in an intermediate state and a malignant cell subset with unique characteristics among pancreatic, ovarian and gastric cancer [79, 87,88,89,90]. Key genes identified during cellular transitions may be involved in transcriptional or posttranscriptional regulation and have the potential to serve as therapeutic targets. Among various malignant tumor cells, an analysis of the mean-squared displacement, velocity and maximal distance traveled contributes to the prediction of the migratory capacity of certain subpopulations, thus providing insights into the mechanisms of tumor metastasis. Unsupervised analyses of the transcriptional trajectory of malignant cells from Monocle2 may be useful for analyzing the dynamics of tumors. For example, malignant osteoclasts are split into progenitor cells, immature cells and mature cells in osteosarcoma [91].

The TME determines phenotypic features, thus enabling the best adaptation to the external environment. For example, malignant cells near the tumor tissue present characteristics of metastasis and invasion, while the malignant cells within the tumor maximize their proliferative ability by activating metabolism. Similar to next-generation sequencing, scRNA-seq can reveal phenotypic dynamics at different stages of tumor progression. Comparison of sequencing data from primary tumor lesions at different clinical stages, recurrent lesions, distant metastatic lesions and corresponding healthy tissue contributes to our understanding of the phenotypic changes that occur during tumor progression and metastasis, such as those in glioma, sarcoma, colorectal cancer and bladder cancer [92,93,94]. Phenotypic heterogeneity might be driven by transcriptomic heterogeneity through its effects on cancer-associated pathways. For example, a recent study revealed that malignant osteoblastic cells, which are characterized by high mesenchymal marker and transcription factor expression, may originate from any type of cell along the osteogenic differentiation pathway from mesenchymal stem cells in osteosarcoma [91]. MUC6 + TFF2 + spasmolytic polypeptide-expressing metaplasia, which may originate from gastric chief cells, is a state of precancerous lesions in gastric cancer [89]. In addition, oxidative phosphorylation plays a key role in breast cancer metastasis based on a comparison of primary tumors and micrometastatic tissue [95]. More key findings on the cell map of malignant cells are listed in Table 3.

Table 3 Key findings related to malignant cell heterogeneity among tumors obtained using single-cell sequencing

CTCs and CSCs

CTCs, rare cancer cells that slough off from primary tumor tissues and circulate in the bloodstream, are thought to act as ‘seeds’ that initiate cancer progression and metastasis [96, 97]. Molecular analysis of CTCs at a single-cell resolution allows the generation of robust, rich genome and transcriptome datasets, thereby providing biomarkers to understand the underlying mechanisms of progression, prognosis, evaluation dynamics and therapeutic response [98,99,100]. CTCs and circulating tumor DNA have been approved by the US Food and Drug Administration as noninvasive liquid biopsies, and extracellular vesicles, circulating tumor RNA and tumor-educated platelets have been identified as novel constituents with promising potential in cancer management [99]. New platforms (e.g., Cheimera X-il20 and Hydro-Seq) were developed to overcome current limitations in the detection of CTCs, including cell loss, a high false-positive detection rate, CTC scarcity and substantial contamination with blood cells [101, 102].

Based on accumulating evidence, CTCs travel in clusters rather than in isolation in the peripheral fluid [103]. The ability to provide insight into CTC subtypes and intravascular interactions with other blood cells may help elucidate the mechanisms of tumor progression and prognosis. In breast cancer, researchers identified 2 CTC subpopulations with differences in estrogen responsiveness and EMT, as well as the interactions between CTCs and peripheral monocytes [104]. Moreover, transforming growth factor β secreted by activated platelets is associated with an EMT phenotype in CTCs, contributing to escape from immune surveillance [97]. Furthermore, another scRNA-seq study revealed the presence of a neutrophil-CTC niche in peripheral blood. Neutrophils from this niche were found to express genes associated with the cell cycle and cytokines, such as TNF-β, OSM, IL-1β, IL-6 and Vcam1, at high levels, and these genes may be involved in niche formation [105]. Based on this finding, the CTC-neutrophil clusters in patients may be useful as a positive biomarker to predict disease outcomes, providing novel insights into targeted therapy for cancer (Fig. 2). Another study reported that the perivascular niche may act as a niche for various subtypes of brain tumor stem-like cells, while the microvascular tracks may serve as a path for tumor migration and therapeutic resistance [106].

Fig. 2
figure2

Schematic of tumor metastasis and interactions between circulating tumor cells (CTCs) and peripheral cells. a Tumor metastasis is a complex process that includes invasion of the primary tumor border, intravasation, survival in the circulatory system, extravasation and the formation of a micrometastatic niche in distant tissues. b However, CTCs in the circulatory system dynamically interact with peripheral cells, and CNCs are very important. E: estrogen; CTCs: circulating tumor cells; CNCs: circulating tumor cell and neutrophil niches

CSCs exhibit self-renewal and multidirectional differentiation, and cancer stemness can be induced by chemotherapy rather than pure selection [107, 108]. Several signaling pathways (e.g., WNT, NOTCH, HIPPO, and RAS), hypoxia, and pathways related to DNA repair and the cell cycle were shown to be functionally associated with the induction of cancer stemness [109,110,111]. More recently, low proteasome gene expression was reported to promote the formation of CSCs via the NOTCH signaling pathway in malignancies [112]. Inhibition of the proteasome drove cancer cells to adopt a CSC state via the TGF-β signaling pathway [113, 114]. A subpopulation of CD24 + CD44 + cells was identified and found to be associated with cancer stemness in HCC [114]. Intriguingly, CSCs from distinct cancers exhibit variability with respect to surface markers, which increases the difficulty of developing CSC-targeted therapies [115].

Stromal heterogeneity

CAFs are major components of stromal cells in several tumors and influence the stroma by secreting excess extracellular matrix and interfering with collagen crosslinking, thus modulating tumor stiffness and facilitating cancer progression [116,117,118,119,120,121]. For example, cadherin 11 promotes extracellular matrix deposition to support the growth of pancreatic carcinoma and resistance to gemcitabine [122]. Moreover, CAF-derived factors may alter the immune microenvironment by inhibiting immune effector cell activity and recruiting immune-suppressive cells, allowing cancer cells to evade immune surveillance [123,124,125,126,127,128]. A recent sequencing analysis separated CAFs into 3 main subgroups: myofibroblastic CAFs, inflammatory CAFs and antigen-presenting CAFs. Specifically, a recent study revealed recurring, patient-dependent expression programs in stromal cells from primary hepatic duct carcinoma and hepatic metastatic tumors [129, 130].

Immune cell heterogeneity and the immune microenvironment

The tumor immune microenvironment, which is an important component of the TME, exerts profound effects on the immunotherapeutic response and clinical outcome. The map of the immune atlas describes a growing portfolio of immune cells, including lymphocytes, monocytes, macrophages, natural killer cells and dendritic cells (DCs). Indeed, subpopulations comprising the immune microenvironment vary during tumor progression. For example, stromal and immune cells undergo dynamic ontological and functional changes that create a protumor and immunosuppressive microenvironment. Resident myeloid cells are gradually replaced with monocyte-derived macrophages and DCs upon T cell exhaustion in metastatic lung adenocarcinoma [68]. ScRNA-seq was applied to study promotion of the immunosuppressive microenvironment through changes such as the increased expression of protein disulfide isomerase family A member 3 (PDIA3) associated with PTEN loss and EGFR amplification in gliomas [131]. Additionally, the cellular composition is associated with prototypic genetic lesions, as observed for the abundance of FLT3-ITD and abundant progenitor-like cells in acute myelocytic leukemia [9]. Research on the mechanisms of tumor heterogeneity has the potential for use in stratified targeted therapy according to tumor category and stage. Moreover, we have summarized recent findings of the tumor immune microenvironment in Table 4.

Table 4 Key findings related to immune cells within various tumor lesions obtained using single-cell sequencing

T cells

Distinct types of T cells orchestrate antitumor T cell responses to fight malignancies in cooperation with other immune cells. Tumor-infiltrating lymphocytes (TILs) are a highly heterogeneous population in terms of cell type composition, molecular subtype, gene and protein expression and functional properties at different stages; thus, a systematic interrogation of TILs is key to the development of immunotherapies [132]. Importantly, scRNA-seq provides information about cellular distribution, clonal amplification, migration and evolution dynamics, as well as a map of the immune landscape through TCR analysis. According to recent studies, some TILs, acting as ‘bystanders,’ are not capable of recognizing antigen-bound major histocompatibility complex (MHC) molecules and then attacking tumor cells, suggesting that the TIL content is not related to the predicted responsiveness of immunotherapy among tumors. Aoki et al. identified a disease-defining T cell subpopulation with high expression of LAG3, which functions to mediate immunosuppression in classic Hodgkin lymphoma [133]. In tumor tissues, regulatory T cells (Tregs) undergo abnormal changes in expression, such as the change in LAG3 expression observed in classic Hodgkin lymphoma, upregulated LAYN expression in FOXP3 + Helios + Tregs, and changes in CCR8 and IL1R2 expression in HCC [134, 135]. In addition, the chronic remodeling of tumor antigens alters Treg cell frequencies and subsequently promotes the recurrence of HCC [136, 137].

T cells are the main components of the adaptive immune system responsible for antitumor immunity and responsiveness to immunotherapy. Single-cell analyses ignore the complex interactions between different cells, and by analyzing the correlations of ligand-receptor pair expression levels among various cell types, we can decipher the landscape of these cellular interactions [138, 139]. Heterodimeric TCRs that consist of two different subunits determine biological functions by interacting with various ligands, and the genetic recombination of subunits creates a diverse TCR repertoire [140]. Moreover, tumor neoantigens must be presented by MHCs and then recognized by TCRs on TILs. The combined use of scRNA-seq with TCR sequencing enables the identification of rare immune subpopulations and their potential functions and links T cell phenotypes with the specific TCR clonotypes of individual T cells. For example, the newly identified CD4 + CXCL13 + BHLHe40 + Th1-like CD4 + subset might explain the difference in responses to anti-PD1 therapy in patients with colorectal cancer presenting with MSI and MSS, while PD-1high CD4 + CD8 + T cells are associated with prolonged survival in patients with HCC [141,142,143]. Integrated scRNA-seq and scTCR-seq analyses revealed the clonotypic expansion of effector-like T cells in tumors and tumor-adjacent tissues, even in peripheral blood. In addition, in immune circulation, intratumor T cells are replenished with fresh, nonexhausted cells from outside the tumor instead of cells within the tumors [144].

B cells

Malignant B cells exhibiting specific transcriptomic profiles are mainly present in lymphoma. The newly identified continuum of cyclic germinal center B cell transitional states reveals the heterogeneity in follicular lymphoma [145, 146]. However, B cell heterogeneity is somewhat determined by somatic mutations, indicating the induction of other mechanisms, including phenotypic diversity or epigenetic modifications [145]. Integrated single-cell sequencing and trajectory analyses also enabled researchers to decipher the evolutionary pathway of malignant B cell maturation, which is accompanied by the progressive loss of follicular and germinal center B cell gene expression programs (e.g., KMT2D, CREBBP and EZH2) [146]. Other researchers further explored the relationship between intratumor heterogeneity and treatment response using a combination of scRNA-seq with transcriptome-informed flow cytometry and identified subpopulation-specific drug sensitivity in nodal B cell lymphoma [147].

Monocytes

Myeloid cells, including tumor-associated macrophages (TAMs) and DCs, have been demonstrated to control tumor malignancy [148]. Myeloid cells exhibit changes in transcriptional pattern and infiltration extent and show great heterogeneity among various tumors.

TAMs display noticeable plasticity in their phenotypic and functional properties after their derivation from the mononuclear phagocyte system. To the best of our knowledge, TAMs undergo polarized activation upon distinct exposure. Some single-cell sequencing studies demonstrated that the classically activated (M1) macrophage signature and alternatively activated (M2) macrophage signature could coexist in single macrophages, thus identifying various subsets of myeloid cells [136, 149]. Apart from the traditional M1/M2 activation paradigm in macrophages, scRNA-seq has revealed the complexity of macrophage expression profiling. For example, scRNA-seq with CITE-seq revealed that TAMs derived from microglia (Mg-TAMs) or classical monocytes (Mo-TAMs) exhibit a spectrum of transcriptional activation states. However, they exhibited a convergent angiogenic and T cell-suppressive capacity ex vivo in glioblastoma [149].

Although DCs represent only a small proportion of leukocytes, they play an important role via their strong antigen-presentation capacity. ScRNA-seq analysis of the intratumoral heterogeneity of DC subsets identified transcriptomic signatures and the prognostic value of DCs [150]. For example, LAMP3 + DC clusters, which migrate from tumors to hepatic lymph nodes, appear to be the most active immune regulators due to their secretion of the most distinct immune-related ligands and modulation of the phenotypes of lymphocytes [33]. These findings indicate that targeting specific TAM and DC subsets may hold significant diagnostic, therapeutic and prognostic potential.

Immunotherapy

In recent years, the development of immunotherapy has significantly enhanced cancer therapies, and immune checkpoint inhibitors (ICIs) have had tremendous effects on some advanced tumors [151]. However, the clinical responses to immunotherapy vary among different patients and cancer types, as evidenced by the limited clinical success rate in patients with HCC [152]. Furthermore, ICIs alone were reported to display limited efficacy, ranging from 15 to 30%, except in melanoma [153]. ScRNA-seq and various multimodal techniques derived from scRNA-seq are advancing our understanding of human tumor heterogeneity and the mechanisms that drive responsiveness and resistance to immunotherapies, thereby providing insights into potential combinational therapies for different cancers [36].

Exhausted T cells are characterized by the high expression of inhibitory receptors, including PD-1, CTLA-4, TIM3 and LAG3, and hierarchical loss of effector functions, which is the target of ICI therapy [154]. Activated CD8 + T cells that express XCL1 at high levels are characterized as in a potential “pre-exhaustion” status and may function by recruiting cDC1 cells [136]. Moreover, the heterogeneity of CD4 + T cells might partially explain their distinct responses to tumor antigens and viral infection [155]. Researchers have identified other immune cells that participate in immunotherapy. For example, lenvatinib presents antitumor activity by reducing TAM infiltration and activating the interferon pathway in HCC [156]. Improved vascularity was shown to result in an improved pancreatic ductal adenocarcinoma (PDAC) prognosis, possibly due to the increased infiltration of immune cells and/or delivery of drugs [157]. These profound results have contributed to the screening of specific targets for immunotherapy. By performing single-cell sequencing in patients treated with ICIs, we were able to analyze the specific transcriptional activity of ICIs or select neoantigens transcribed at a high level, enabling the design of precise therapy. By analyzing the heterogeneity of immune-related genes using scRNA-seq, we can design combination therapies that target various tumor neoantigens and thus improve clinical efficacy.

A comprehensive understanding of the immune cell composition and states is crucial to delineating the responsiveness and resistance to immunotherapies and could help in the design of novel targeted immune-modulating therapies. Notably, the identification of transcriptome-wide signatures associated with therapeutic response or resistance improves our understanding of the mechanisms of immunotherapy. Extending the transcriptional immune atlas of normal tissue to the TME could provide intriguing insights into the heterogeneity and distinct immunotherapeutic responses of a spectrum of tumors. For patients who are insensitive to immunotherapy (such as CTLA4 and PD1/PD-L1), single-cell sequencing might be able to analyze specific tumor tissues and elucidate the underlying mechanisms of resistance. For example, a new subset of macrophages with high CD73 expression was shown to be associated with a reduction in immune cell infiltration. Therefore, targeted therapy could combine CD73 blockers with PD-1/CTLA-4, a potential strategy of clinical significance [158]. Another example is the construction of an immune checkpoint gene network in patients with follicular lymphoma who were insensitive to CTLA-4/PD-1 therapy, which improved our understanding of the complex mechanisms underlying immune invasion and provided new insights into therapy design [145].

According to Darwinian theory, resistant cells are thought to arise from selection pressure on heterogeneous malignant cells. However, recent studies have shown that simple selection does not adequately account for late relapse, and resistance patterns (e.g., transcriptomic reprogramming) may exist before treatment across a spectrum of tumors [159,160,161,162,163]. In a PD-1-resistant colorectal cancer patient with high microsatellite instability, biallelic loss of the beta2-microglobulin (β2M) gene was found to be associated with intrinsic resistance [164]. Moreover, a precise pattern, including decreased NF-κB-binding signatures, followed by a rapid reduction in the regulatory activity of transcription factors (e.g., EBF1, FOXM1 and IRF4), was revealed in chronic lymphocytic leukemia patients with ibrutinib resistance [165,166,167]. This approach disentangles the dispute over whether resistance is the result of selection or transcriptional adaption, providing valuable insight into the development of drug resistance and the application of stage-specific biomarkers. Nevertheless, epigenetic alterations, including those due to KDM5 histone demethylase activity, were identified as correlated with therapeutic resistance by Kunihiko [168].

ScRNA-seq has also been used to explore the novel combination of immunotherapy with chemical or radiation therapy to improve clinical effects among patients with cancer and explore the relationship of resistance between distinct therapies [169, 170]. For example, radioresistant cells from basal breast cancer with a high tumor mutation burden, high degree of mutational microsatellite instability and activated NRF2 pathways exhibit a higher rate of PD-L1 positivity [171]. In TME-modulation strategies, traditional delivery systems limit drug retention times, and nanoparticles with unique physical properties and elaborate designs efficiently penetrate the TME and specifically deliver drugs to targeted components of the TME among a spectrum of tumors. However, researchers’ interest in recent years has increasingly focused on the toxicity of monoclonal antibodies. A recent study revealed the genes (e.g., SPP1, HMOX1, TIMP1 and NAMPT) associated with trastuzumab-mediated cardiotoxicity in breast cancer [172]. These findings are useful for identifying potential prognostic biomarkers and expanding the immunotherapy scope based on patient subtype and dominant immune cell crosstalk, paving the way for the use of combination therapy in patients with cancer.

Diagnosis

Cancers are traditionally diagnosed based on tissue origin and histologic features. Growing evidence has shown that molecular features influence the TME and thus alter clinical behaviors and responses to therapy. A better understanding of molecular features through identifying sensitive biomarkers, mutations or gene expression profiles will improve the diagnosis and targeted therapy of cancer in patients. For example, a malignant cell subpopulation dominating the metastatic stage was demonstrated to be a biomarker in lung cancer, while a tumor-associated microglia/macrophage-mediated EGFR/ERBB2 feedback-crosstalk signaling module was proven to outperform traditional gene biomarkers in glioma [68, 173]. Another potential diagnostic direction involves the molecular subtype among various tumors, such as bladder cancer and pancreatic cancer [174]. A recent study in bladder cancer correlated scRNA-seq data with previously known molecular subtype information (e.g., luminal-papillary, luminal-infiltrated, luminal, basal-squamous and neuronal) and revealed the correlation between molecular and clinical features, guiding molecular diagnosis and targeted therapy [175]. In addition, the detection of CTCs by single-cell sequencing technology contributes to early diagnosis and prognostic monitoring.

Treatment

Single-cell sequencing is widely utilized to measure the clinical effectiveness and safety of novel drugs in clinical trials. A search for ‘cancer’ and ‘single-cell sequencing’ on the http://clinicaltrails.gov Web site yielded 16 relevant clinical trial records (Table 5). Most of these records described phase 2 clinical trials. Single-cell sequencing can be used to explore dynamic regulatory networks and identify heterogeneous cellular behaviors in response to various chemical drugs or the irradiation of cell lines or tumor samples, such as esophageal squamous cell carcinoma samples [176]. The newly developed technique sci-Plex, which is based on Sci-Seq and nuclear hashing, has been used to decipher transcriptomic changes in millions of malignant cells toward distinct chemical drugs and the underlying mechanisms. Compared with traditional high-throughput chemical screens, sci-Plex can delineate more nuanced molecular mechanisms and reveal transcriptomic trajectories behind drug responses at a single-cell resolution with a lower cost [177].

Table 5 Clinical trials for cancer treatments associated with single-cell sequencing

Angiogenesis-targeted therapy has been demonstrated to play a key role in modulating tumor heterogeneity and tumor liver metastasis, and the underlying metastatic patterns were recently revealed by scRNA-seq [178,179,180]. The combination of VEGF blockade and tyrosine kinase inhibitors has shown promising clinical effects in advanced tumors, such as HCC and pancreatic neuroendocrine tumors [181, 182]. Complex growth patterns may explain why the antiangiogenic therapy efficacy varies among patients and thereby may function as a stratification method to recognize populations who might experience a benefit.

Conclusions

Single-cell sequencing has advanced at a very rapid speed, and emerging and powerful technologies will undoubtedly promote new levels of precision and accuracy in molecular cancer research, such as the tumor microenvironment and heterogeneity. Therefore, it significantly improved our understanding of cancer diagnostic stratification, biomarkers, precise treatment and prognosis prediction [183,184,185,186,187,188].

However, the limitations of single-cell sequencing should be recognized. First, scRNA-seq intrinsically shows that not all eukaryotic cells undergo transcription at a consistent basal rate. Transcription occurs in pulses; therefore, instant sequencing is unable to completely decipher the transcription map [189, 190]. Additionally, most sequencing methods are designed for 3′ or 5′ reads and are not sensitive to transcripts at low abundance. Second, scRNA-seq alone cannot correlate genotype and phenotype, suggesting the need for high-throughput, low-cost multiomic technologies to delineate the overall tumor tissue landscape. Third, batch effects derived from the use of different platforms and processing procedures and analysis on different days might exist. Such batch effects are extremely clearly when different data from different sequencing studies are analyzed. Finally, this type of sequencing analysis is challenging to implement in larger cohorts; thus, existing findings should be applied with caution and confirmed in clinical trials. Moreover, the individual differences among patients and different platforms applied in distinct trials limit the reliability of the results described above.

Multiomics integrates high-throughput biomolecular data related to multiple factors, including the genome, transcriptome, proteome, interactome, epigenome and metabolism, among which the transcriptome is central. The multiomics data promote the interrogation of complex interactions and connections in tumors at different states and with different phenotypes and also provides additional nuanced data on the dynamic progression of tumor initiation, progression, growth, immune evasion, metastasis, relapse and therapy resistance. The integration of multiomics methods improves the technical per-cell reliability and sensitivity, which results in superior accuracy, robustness, compatibility and expandability and even improves the preamplification procedure. Overall, these data contribute to establishing a complex computational framework, which enables the de novo discovery of both continuous and discrete expression patterns from single-cell sequencing data.

In conclusion, the integration of sequencing techniques can generate more complex, high-throughput information, including genomic, epigenomic, transcriptomic, proteomic, spatial and temporal data, necessitating the development of powerful and precise models or algorithms to provide new strategies for diagnosis, classification, targeted treatment and prognosis prediction. Notably, the accuracy and sensitivity of emerging technologies and computational analyses must be improved, and the costs should become more affordable in the near future.

Availability of data and materials

Not applicable; all information in this review can be found in the reference list.

Abbreviations

SCS:

Single-cell sequencing

snRNA-seq:

Single-nucleus RNA sequencing

Sci-Seq:

Single-cell genomes with combinatorial indexing

RT:

Reverse transcription

SPLiT-seq:

Split-pool ligation-based transcriptome sequencing

FACS:

Fluorescence-activated cell sorting

MARS-seq:

Massively parallel single-cell RNA sequencing

inDrop:

Indexing drop RNA sequencing

ScATAC-seq:

Single-cell transposase-accessible chromatin using sequencing

CoBATCH:

Combinatorial barcoding and targeted chromatin release

PAT:

Tn5 transposase to protein A

CITE-seq:

Cellular indexing of transcriptomes and epitopes by sequencing

REAP-seq:

RNA expression and protein sequencing assay

INs-seq:

Intracellular staining and sequencing

SMART:

Switching Mechanism at 5′ End of RNA Template

TSO:

Template switch oligo

IGS:

In situ sequencing

CyTOF:

Mass cytometry by time of flight

IMC:

Imaging mass cytometry

LC–MS:

Liquid chromatography–mass spectrometry

SCoPE-MS:

Single-cell proteomics by mass spectrometry

t-SNE:

T-distributed Stochastic Neighbor Embedding

PCA:

Principal component analysis

CCA:

Canonical correlation analysis

CNV:

Copy number variation

DEG:

Differently expressed gene

CAF:

Cancer-associated fibroblast

GA:

Gastric adenocarcinoma

LCM:

Laser-capture microdissection

LINNAEUS:

Lineage tracing by nuclease-activated editing of ubiquitous sequences

TCR:

T cell receptor

ScRNA-seq:

Single-cell RNA sequencing

SNV:

Single-nucleotide variation

CNV:

Copy number variation

Sv:

Structural variation

CTC:

Circulating tumor cell

CSC:

Cancer stem cell

TME:

Tumor microenvironment

TGF-β1:

Transforming growth factor β1

PDIA3:

Protein disulfide isomerase family A member 3

PDAC:

Pancreatic ductal adenocarcinoma

HCC:

Hepatocellular carcinoma

ESCC:

Esophageal squamous cell carcinoma

CAF:

Cancer-associated fibroblast

TIL:

Tumor-infiltrating lymphocyte

MSI:

Microsatellite instability

MSS:

Microsatellite stability

PD-1:

Programmed death 1

CTLA-4:

Cytotoxic T-lymphocyte-associated protein 4

DC:

Dendritic cell

cDC:

Conventional type 1 dendritic cell

Treg:

Regulatory T cell

MIAT:

Myocardial infarction associated transcript

CRISP-seq:

ScRNA-seq on cluster regulatory interspaced short palindromic repeats–pooled screens

TAM:

Tumor-associated macrophages

M1:

Classically activated macrophages

M2:

Alternatively activated macrophages

EMT:

Epithelial-to-mesenchymal transition

MET:

Mesenchymal-to-epithelial transition

ICI:

Immune checkpoint inhibitor

SmFISH:

Single-molecule RNA fluorescence in situ hybridization

G&T-Seq:

Genome and transcriptome sequencing

DR-Seq:

DNA-mRNA sequencing

T-ATAC-seq:

Transcript-indexed ATAC-seq

CORTAD-seq:

Concurrent sequencing of the transcriptome and targeted genomic regions

scDam&T-seq:

Single-cell DNA adenine methyltransferase identification with messenger RNA sequencing

References

  1. 1.

    Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6(5):377–82.

    CAS  Article  Google Scholar 

  2. 2.

    Navin N, Kendall J, Troge J, Andrews P, Rodgers L, McIndoo J, et al. Tumour evolution inferred by single-cell sequencing. Nature. 2011;472(7341):90–4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Paolillo C, Londin E, Fortina P. Single-cell genomics. Clin Chem. 2019;65(8):972–85.

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Zafar H, Wang Y, Nakhleh L, Navin N, Chen K. Monovar: single-nucleotide variant detection in single cells. Nat Methods. 2016;13(6):505–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Tirosh I, Venteicher AS, Hebert C, Escalante LE, Patel AP, Yizhak K, et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature. 2016;539(7628):309–13.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  6. 6.

    Venteicher AS, Tirosh I, Hebert C, Yizhak K, Neftel C, Filbin MG, et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science. 2017;355(6332):eaai8478.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. 7.

    Lake BB, Ai R, Kaeser GE, Salathia NS, Yung YC, Liu R, et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science. 2016;352(6293):1586–90.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Rosenberg AB, Roco CM, Muscat RA, Kuchina A, Sample P, Yao Z, et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science. 2018;360(6385):176–82.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    van Galen P, Hovestadt V, Wadsworth Ii MH, Hughes TK, Griffin GK, Battaglia S, et al. Single-cell RNA-Seq reveals AML hierarchies relevant to disease progression and immunity. Cell. 2019;176(6):1265–81.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  10. 10.

    Zilionis R, Engblom C, Pfirschke C, Savova V, Zemmour D, Saatcioglu HD, et al. Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. Immunity. 2019;50(5):1317–34.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Baslan T, Hicks J. Unravelling biology and shifting paradigms in cancer with single-cell sequencing. Nat Rev Cancer. 2017;17(9):557–69.

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Zhang L, Li Z, Skrzypczynska KM, Fang Q, Zhang W, O’Brien SA, et al. Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer. Cell. 2020;181(2):442–59.

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA. The technology and biology of single-cell RNA sequencing. Mol Cell. 2015;58(4):610–20.

    CAS  Article  Google Scholar 

  14. 14.

    O’Flanagan CH, Campbell KR, Zhang AW, Kabeer F, Lim JLP, Biele J, et al. Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol. 2019;20(1):210.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  15. 15.

    Zhang X, Li T, Liu F, Chen Y, Yao J, Li Z, et al. Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-Seq systems. Mol Cell. 2019;73(1):130–42.

    PubMed  Article  Google Scholar 

  16. 16.

    Ding J, Adiconis X, Simmons SK, Kowalczyk MS, Hession CC, Marjanovic ND, et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol. 2020;38(6):737–46.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Vitak SA, Torkenczy KA, Rosenkrantz JL, Fields AJ, Christiansen L, Wong MH, et al. Sequencing thousands of single-cell genomes with combinatorial indexing. Nat Methods. 2017;14(3):302–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Han X, Wang R, Zhou Y, Fei L, Sun H, Lai S, et al. Mapping the mouse cell atlas by Microwell-Seq. Cell. 2018;172(5):1091–107.

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A, Smets M, et al. Comparative analysis of single-cell RNA sequencing methods. Mol Cell. 2017;65(4):631–43.

    CAS  Article  Google Scholar 

  20. 20.

    Jeyaprakash JD, Samuel S, Ruhe J. A facile photochemical surface modification technique for the generation of microstructured fluorinated surfaces. Langmuir. 2004;20(23):10080–5.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  21. 21.

    Singh S, Wang L, Schaff DL, Sutcliffe MD, Koeppel AF, Kim J, et al. In situ 10-cell RNA sequencing in tissue and tumor biopsy samples. Sci Rep. 2019;9(1):4836.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  22. 22.

    Woo J, Winterhoff BJ, Starr TK, Aliferis C, Wang J, et al. De novo prediction of cell-type complexity in single-cell RNA-seq and tumor microenvironments. Life Sci Alliance. 2019. https://doi.org/10.26508/lsa.201900443.

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Levitin HM, Yuan J, Cheng YL, Ruiz FJ, Bush EC, Bruce JN, et al. De novo gene signature identification from single-cell RNA-seq with hierarchical Poisson factorization. Mol Syst Biol. 2019. https://doi.org/10.15252/msb.20188557.

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Xie P, Gao M, Wang C, Zhang J, Noel P, Yang C, et al. SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles. Nucleic Acids Res. 2019. https://doi.org/10.1093/nar/gkz116.

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Hong M, Tao S, Zhang L, Diao LT, Huang X, Huang S, et al. RNA sequencing: new technologies and applications in cancer research. J Hematol Oncol. 2020;13(1):166.

    PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Ramskold D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30(8):777–82.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  27. 27.

    Picelli S, Bjorklund AK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10(11):1096–8.

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Song Y, Milon B, Ott S, Zhao X, Sadzewicz L, Shetty A, et al. A comparative analysis of library prep approaches for sequencing low input translatome samples. BMC Genom. 2018;19(1):696.

    Article  CAS  Google Scholar 

  29. 29.

    Bell OH, Copland DA, Ward A, Nicholson LB, Lange CAK, Chu CJ, et al. Single eye mRNA-Seq reveals normalisation of the retinal microglial transcriptome following acute inflammation. Front Immunol. 2019;10:3033.

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Bjorklund AK, Forkel M, Picelli S, Konya V, Theorell J, Friberg D, et al. The heterogeneity of human CD127(+) innate lymphoid cells revealed by single-cell RNA sequencing. Nat Immunol. 2016;17(4):451–60.

    PubMed  Article  CAS  Google Scholar 

  31. 31.

    Dong X, Wang F, Liu C, Ling J, Jia X, Shen F, et al. Single-cell analysis reveals the intra-tumor heterogeneity and identifies MLXIPL as a biomarker in the cellular trajectory of hepatocellular carcinoma. Cell Death Discov. 2021;7(1):14.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Wang X, He Y, Zhang Q, Ren X, Zhang Z. Direct comparative analyses of 10X genomics chromium and smart-seq2. Genom Proteomics Bioinform. 2021. https://doi.org/10.1016/j.gpb.2020.02.005.

    Article  Google Scholar 

  33. 33.

    Zhang Q, He Y, Luo N, Patel SJ, Han Y, Gao R, et al. Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell. 2019;179(4):829–45.

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    Coons AH, Creech HJ, Jones RN. Immunological properties of an antibody containing a fluorescent group. Proc Soc Exp Biol Med. 1941;47(2):200–2.

    CAS  Article  Google Scholar 

  35. 35.

    Gall JG, Pardue ML. Formation and detection of Rna-DNA hybrid molecules in cytological preparations. Proc Natl Acad Sci USA. 1969. https://doi.org/10.1073/pnas.63.2.378.

    Article  PubMed  Google Scholar 

  36. 36.

    Geng S, Wang J, Zhang X, Zhang JJ, Wu F, Pang Y, et al. Single-cell RNA sequencing reveals chemokine self-feeding of myeloma cells promotes extramedullary metastasis. FEBS Lett. 2020;594(3):452–65.

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Jackson HW, Fischer JR, Zanotelli VRT, Ali HR, Mechera R, Soysal SD, et al. The single-cell pathology landscape of breast cancer. Nature. 2020;578(7796):615–20.

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Moncada R, Barkley D, Wagner F, Chiodin M, Devlin JC, Baron M, et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat Biotechnol. 2020;38(3):333–42.

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Stahl PL, Salmen F, Vickovic S, Lundmark A, Navarro JF, Magnusson J, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353(6294):78–82.

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Spanjaard B, Hu B, Mitic N, Olivares-Chauvet P, Janjuha S, Ninov N, et al. Simultaneous lineage tracing and cell-type identification using CRISPR-Cas9-induced genetic scars. Nat Biotechnol. 2018;36(5):469–73.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Quinn JJ, Jones MG, Okimoto RA, Nanjo S, Chan MM, Yosef N, et al. Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts. Science. 2021. https://doi.org/10.1126/science.abc1944.

    Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Velten L, Story BA, Hernandez-Malmierca P, Raffel S, Leonce DR, Milbank J, et al. Identification of leukemic and pre-leukemic stem cells by clonal tracking from single-cell transcriptomics. Nat Commun. 2021;12(1):1366.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Grindberg RV, Yee-Greenbaum JL, McConnell MJ, Novotny M, O’Shaughnessy AL, Lambert GM, et al. RNA-sequencing from single nuclei. Proc Natl Acad Sci USA. 2013;110(49):19802–7.

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Habib N, Li Y, Heidenreich M, Swiech L, Avraham-Davidi I, Trombetta JJ, et al. Div-Seq: single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science. 2016;353(6302):925–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Habib N, Avraham-Davidi I, Basu A, Burks T, Shekhar K, Hofree M, et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat Methods. 2017;14(10):955–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Lacar B, Linker SB, Jaeger BN, Krishnaswami SR, Barron JJ, Kelder MJE, et al. Nuclear RNA-seq of single neurons reveals molecular signatures of activation. Nat Commun. 2016;7:11022.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Velmeshev D, Schirmer L, Jung D, Haeussler M, Perez Y, Mayer S, et al. Single-cell genomics identifies cell type-specific molecular changes in autism. Science. 2019;364(6441):685–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Wolfien M, Galow AM, Muller P, Bartsch M, Brunner RM, Goldammer T, et al. Single-nucleus sequencing of an entire mammalian heart: cell type composition and velocity. Cells. 2020. https://doi.org/10.3390/cells9020318.

    Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Wu H, Kirita Y, Donnelly EL, Humphreys BD. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: rare cell types and novel cell states revealed in fibrosis. J Am Soc Nephrol. 2019;30(1):23–32.

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    Tosti L, Hang Y, Debnath O, Tiesmeyer S, Trefzer T, Steiger K, et al. Single-nucleus and in situ RNA-sequencing reveal cell topographies in the human pancreas. Gastroenterology. 2021. https://doi.org/10.1053/j.gastro.2020.11.010.

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Habib N, Avraham-Davidi I, Basu A, Burks T, Shekhar K, Hofree M, et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat Methods. 2017. https://doi.org/10.1038/Nmeth.4407.

    Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Slyper M, Porter CBM, Ashenberg O, Waldman J, Drokhlyansky E, Wakiro I, et al. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat Med. 2020;26(5):792–802.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Rodriguez-Meira A, Buck G, Clark SA, Povinelli BJ, Alcolea V, Louka E, et al. Unravelling intratumoral heterogeneity through high-sensitivity single-cell mutational analysis and parallel RNA sequencing. Mol Cell. 2019;73(6):1292–305.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    McCarthy DJ, Rostom R, Huang Y, Kunz DJ, Danecek P, Bonder MJ, et al. Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes. Nat Methods. 2020;17(4):414–21.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  55. 55.

    Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods. 2013;10(12):1213–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Satpathy AT, Saligrama N, Buenrostro JD, Wei Y, Wu B, Rubin AJ, et al. Transcript-indexed ATAC-seq for precision immune profiling. Nat Med. 2018;24(5):580–90.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Bartosovic M, Kabbe M, Castelo-Branco G. Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nat Biotechnol. 2021. https://doi.org/10.1038/s41587-021-00869-9.

    Article  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Payne AC, Chiang ZD, Reginato PL, Mangiameli SM, Murray EM, Yao CC, et al. In situ genome sequencing resolves DNA sequence and structure in intact biological samples. 2021. Science. https://doi.org/10.1126/science.aay3446.

  59. 59.

    Tan L, Ma W, Wu H, Zheng Y, Xing D, Chen R, et al. Changes in genome architecture and transcriptional dynamics progress independently of sensory experience during post-natal brain development. Cell. 2021;184(3):741–58.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  60. 60.

    Veenstra J, Dimitrion P, Yao Y, Zhou L, Ozog D, Mi QS. Research techniques made simple: use of imaging mass cytometry for dermatological research and clinical applications. J Investig Dermatol. 2021;141(4):705–12.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  61. 61.

    Cheung TK, Lee CY, Bayer FP, McCoy A, Kuster B, Rose CM. Defining the carrier proteome limit for single-cell proteomics. Nat Methods. 2021;18(1):76–83.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  62. 62.

    Markodimitraki CM, Rang FJ, Rooijers K, de Vries SS, Chialastri A, de Luca KL, et al. Simultaneous quantification of protein-DNA interactions and transcriptomes in single cells with scDam&T-seq. Nat Protoc. 2020;15(6):1922–53.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Kong SL, Li H, Tai JA, Courtois ET, Poh HM, Lau DP, et al. Concurrent single-cell RNA and targeted DNA sequencing on an automated platform for comeasurement of genomic and transcriptomic signatures. Clin Chem. 2019;65(2):272–81.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  64. 64.

    Campbell KR, Steif A, Laks E, Zahn H, Lai D, McPherson A, et al. clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers. Genome Biol. 2019;20(1):54.

    PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Rios AC, Capaldo BD, Vaillant F, Pal B, van Ineveld R, Dawson CA, et al. Intraclonal plasticity in mammary tumors revealed through large-scale single-cell resolution 3D imaging. Cancer Cell. 2019;35(4):618–32.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  66. 66.

    Katzenelenbogen Y, Sheban F, Yalin A, Yofe I, Svetlichnyy D, Jaitin DA, et al. Coupled scRNA-Seq and intracellular protein activity reveal an immunosuppressive role of TREM2 in cancer. Cell. 2020;182(4):872–85.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  67. 67.

    Okonechnikov K, Pfister SM, Kutscher LM. Probing medulloblastoma initiation at the single-cell level. Trends Cancer. 2019;5(12):759–61.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  68. 68.

    Kim N, Kim HK, Lee K, Hong Y, Cho JH, Choi JW, et al. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat Commun. 2020;11(1):2285.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. 69.

    Wu J, Xiao Y, Sun J, Sun H, Chen H, Zhu Y, et al. A single-cell survey of cellular hierarchy in acute myeloid leukemia. J Hematol Oncol. 2020;13(1):128.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  70. 70.

    Rozenblatt-Rosen O, Regev A, Oberdoerffer P, Nawy T, Hupalowska A, Rood JE, et al. The human tumor atlas network: charting tumor transitions across space and time at single-cell resolution. Cell. 2020;181(2):236–49.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Liudahl SM, Betts CB, Sivagnanam S, Morales-Oyarvide V, da Silva A, Yuan C, et al. Leukocyte heterogeneity in pancreatic ductal adenocarcinoma: phenotypic and spatial features associated with clinical outcome. Cancer Discov. 2021. https://doi.org/10.1158/2159-8290.Cd-20-0841.

    Article  PubMed  Google Scholar 

  72. 72.

    Bailey P, Chang DK, Nones K, Johns AL, Patch AM, Gingras MC, et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature. 2016;531(7592):47–52.

    CAS  Article  Google Scholar 

  73. 73.

    Collisson EA, Sadanandam A, Olson P, Gibb WJ, Truitt M, Gu S, et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med. 2011;17(4):500–3.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. 74.

    Moffitt RA, Marayati R, Flate EL, Volmar KE, Loeza SGH, Hoadley KA, et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat Genet. 2015;47(10):1168–78.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. 75.

    Cancer Genome Atlas Research Network. Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell. 2017;32(2):185–203.

    Article  CAS  Google Scholar 

  76. 76.

    Karasinska JM, Topham JT, Kalloger SE, Jang GH, Denroche RE, Culibrk L, et al. Altered gene expression along the glycolysis-cholesterol synthesis axis is associated with outcome in pancreatic cancer. Clin Cancer Res. 2020;26(1):135–46.

    CAS  PubMed  Article  Google Scholar 

  77. 77.

    Skvortsova K, Stirzaker C, Taberlay P. The DNA methylation landscape in cancer. Essays Biochem. 2019;63(6):797–811.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. 78.

    Sharma A, Merritt E, Hu X, Cruz A, Jiang C, Sarkodie H, et al. Non-genetic intra-tumor heterogeneity is a major predictor of phenotypic heterogeneity and ongoing evolutionary dynamics in lung tumors. Cell Rep. 2019;29(8):2164–74.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. 79.

    Wouters J, Kalender-Atak Z, Minnoye L, Spanier KI, De Waegeneer M, Bravo Gonzalez-Blas C, et al. Robust gene expression programs underlie recurrent cell states and phenotype switching in melanoma. Nat Cell Biol. 2020;22(8):986–98.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  80. 80.

    Zhang Q, Gu ML, et al. Single-cell sequencing and its application in breast cancer. Yi Chuan. 2020. https://doi.org/10.16288/j.yczz.19-268.

    Article  PubMed  Google Scholar 

  81. 81.

    Li J, Wang R, Zhou X, Wang W, Gao S, Mao Y, et al. Genomic and transcriptomic profiling of carcinogenesis in patients with familial adenomatous polyposis. Gut. 2020;69(7):1283–93.

    CAS  PubMed  Article  Google Scholar 

  82. 82.

    Praktiknjo SD, Obermayer B, Zhu Q, Fang L, Liu H, Quinn H, et al. Tracing tumorigenesis in a solid tumor model at single-cell resolution. Nat Commun. 2020;11(1):991.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. 83.

    Chen YC, Sahoo S, Brien R, Jung S, Humphries B, Lee W, et al. Single-cell RNA-sequencing of migratory breast cancer cells: discovering genes associated with cancer metastasis. Analyst. 2019;144(24):7296–309.

    CAS  PubMed  Article  Google Scholar 

  84. 84.

    Teo YV, Rattanavirotkul N, Olova N, Salzano A, Quintanilla A, Tarrats N, et al. Notch signaling mediates secondary senescence. Cell Rep. 2019;27(4):997–1007.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    Toledo F. Mechanisms generating cancer genome complexity: back to the future. Cancers (Basel). 2020;12(12):3783.

    CAS  Article  Google Scholar 

  86. 86.

    Tawil N, Spinelli C, Bassawon R, Rak J. Genetic and epigenetic regulation of cancer coagulome—lessons from heterogeneity of cancer cell populations. Thromb Res. 2020;191(Suppl 1):S99–105.

    CAS  PubMed  Article  Google Scholar 

  87. 87.

    Cook DP, Vanderhyden BC. Ovarian cancer and the evolution of subtype classifications using transcriptional profilingdagger. Biol Reprod. 2019;101(3):645–58.

    PubMed  Article  Google Scholar 

  88. 88.

    Liu Y, Ye G, Huang L, Zhang C, Sheng Y, Wu B, et al. Single-cell transcriptome analysis demonstrates inter-patient and intra-tumor heterogeneity in primary and metastatic lung adenocarcinoma. Aging (Albany NY). 2020;12(21):21559–81.

    CAS  Article  Google Scholar 

  89. 89.

    Zhang M, Hu S, Min M, Ni Y, Lu Z, Sun X, et al. Dissecting transcriptional heterogeneity in primary gastric adenocarcinoma by single cell RNA sequencing. Gut. 2020. https://doi.org/10.1136/gutjnl-2019-320368.

    Article  PubMed  PubMed Central  Google Scholar 

  90. 90.

    Peng J, Sun BF, Chen CY, Zhou JY, Chen YS, Chen H, et al. Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma. Cell Res. 2019;29(9):725–38.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  91. 91.

    Zhou Y, Yang D, Yang Q, Lv X, Huang W, Zhou Z, et al. Single-cell RNA landscape of intratumoral heterogeneity and immunosuppressive microenvironment in advanced osteosarcoma. Nat Commun. 2020;11(1):6322.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  92. 92.

    Vladoiu MC, El-Hamamy I, Donovan LK, Farooq H, Holgado BL, Sundaravadanam Y, et al. Childhood cerebellar tumours mirror conserved fetal transcriptional programs. Nature. 2019;572(7767):67–73.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  93. 93.

    Tang YJ, Huang J, Tsushima H, Ban GI, Zhang H, Oristian KM, et al. Tracing tumor evolution in sarcoma reveals clonal origin of advanced metastasis. Cell Rep. 2019;28(11):2837–50.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  94. 94.

    Kyrochristos ID, Roukos DH. Comprehensive intra-individual genomic and transcriptional heterogeneity: evidence-based Colorectal Cancer Precision Medicine. Cancer Treat Rev. 2019;80:101894.

    CAS  PubMed  Article  Google Scholar 

  95. 95.

    Davis RT, Blake K, Ma D, Gabra MBI, Hernandez GA, Phung AT, et al. Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing. Nat Cell Biol. 2020;22(3):310–20.

    CAS  PubMed  Article  Google Scholar 

  96. 96.

    Tieng FYF, Baharudin R, Abu N, Mohd Yunos RI, Lee LH, Ab Mutalib NS. Single cell transcriptome in colorectal cancer-current updates on its application in metastasis, chemoresistance and the roles of circulating tumor cells. Front Pharmacol. 2020;11:135.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. 97.

    Yu M, Bardia A, Wittner BS, Stott SL, Smas ME, Ting DT, et al. Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science. 2013;339(6119):580–4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  98. 98.

    Pailler E, Faugeroux V, Oulhen M, Mezquita L, Laporte M, Honore A, et al. Acquired resistance mutations to ALK inhibitors identified by single circulating tumor cell sequencing in ALK-rearranged non-small-cell lung cancer. Clin Cancer Res. 2019;25(22):6671–82.

    CAS  PubMed  Article  Google Scholar 

  99. 99.

    De Rubis G, Rajeev Krishnan S, Bebawy M. Liquid biopsies in cancer diagnosis, monitoring, and prognosis. Trends Pharmacol Sci. 2019;40(3):172–86.

    PubMed  Article  CAS  Google Scholar 

  100. 100.

    Zhu Z, Qiu S, Shao K, Hou Y. Progress and challenges of sequencing and analyzing circulating tumor cells. Cell Biol Toxicol. 2018;34(5):405–15.

    CAS  PubMed  Article  Google Scholar 

  101. 101.

    Cheng YH, Chen YC, Lin E, Brien R, Jung S, Chen YT, et al. Hydro-Seq enables contamination-free high-throughput single-cell RNA-sequencing for circulating tumor cells. Nat Commun. 2019;10(1):2163.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  102. 102.

    Wang PX, Sun YF, Jin WX, Cheng JW, Peng HX, Xu Y, et al. Circulating tumor cell detection and single-cell analysis using an integrated workflow based on ChimeraX(®)-i120 Platform: a prospective study. Mol Oncol. 2020. https://doi.org/10.1002/1878-0261.12876.

    Article  PubMed  PubMed Central  Google Scholar 

  103. 103.

    Aceto N, Bardia A, Miyamoto DT, Donaldson MC, Wittner BS, Spencer JA, et al. Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis. Cell. 2014;158(5):1110–22.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  104. 104.

    Brechbuhl HM, Vinod-Paul K, Gillen AE, Kopin EG, Gibney K, Elias AD, et al. Analysis of circulating breast cancer cell heterogeneity and interactions with peripheral blood mononuclear cells. Mol Carcinog. 2020;59(10):1129–39.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  105. 105.

    Szczerba BM, Castro-Giner F, Vetter M, Krol I, Gkountela S, Landin J, et al. Neutrophils escort circulating tumour cells to enable cell cycle progression. Nature. 2019;566(7745):553–7.

    CAS  Article  Google Scholar 

  106. 106.

    Xiao Y, Kim D, Dura B, Zhang K, Yan R, Li H, et al. Ex vivo dynamics of human glioblastoma cells in a microvasculature-on-a-chip system correlates with tumor heterogeneity and subtypes. Adv Sci (Weinh). 2019;6(8):1801531.

    Article  CAS  Google Scholar 

  107. 107.

    Couturier CP, Ayyadhury S, Le PU, Nadaf J, Monlong J, Riva G, et al. Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy. Nat Commun. 2020;11(1):3406.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  108. 108.

    Chen J, Li Y, Yu TS, McKay RM, Burns DK, Kernie SG, et al. A restricted cell population propagates glioblastoma growth after chemotherapy. Nature. 2012;488(7412):522–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  109. 109.

    Jin L, Vu T, Yuan G, Datta PK. STRAP promotes stemness of human colorectal cancer via epigenetic regulation of the NOTCH pathway. Cancer Res. 2017;77(20):5464–78.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  110. 110.

    Wu H, Li Y, Hou Q, Zhou R, Li Z, Wu S, et al. Singlecell intratumoral stemness analysis reveals the involvement of cell cycle and DNA damage repair in two different types of esophageal cancer. Oncol Rep. 2019. https://doi.org/10.3892/or.2019.7117.

    Article  PubMed  PubMed Central  Google Scholar 

  111. 111.

    Chen YC, Jung S, Zhang Z, Wicha MS, Yoon E. Co-culture of functionally enriched cancer stem-like cells and cancer-associated fibroblasts for single-cell whole transcriptome analysis. Integr Biol (Camb). 2019;11(9):353–61.

    Article  Google Scholar 

  112. 112.

    Lagadec C, Vlashi E, Frohnen P, Alhiyari Y, Chan M, Pajonk F. The RNA-binding protein Musashi-1 regulates proteasome subunit expression in breast cancer- and glioma-initiating cells. Stem Cells. 2014;32(1):135–44.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  113. 113.

    Banno A, Garcia DA, van Baarsel ED, Metz PJ, Fisch K, Widjaja CE, et al. Downregulation of 26S proteasome catalytic activity promotes epithelial-mesenchymal transition. Oncotarget. 2016;7(16):21527–41.

    PubMed  PubMed Central  Article  Google Scholar 

  114. 114.

    Ho DW, Tsui YM, Sze KM, Chan LK, Cheung TT, Lee E, et al. Single-cell transcriptomics reveals the landscape of intra-tumoral heterogeneity and stemness-related subpopulations in liver cancer. Cancer Lett. 2019;459:176–85.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  115. 115.

    Meyer M, Reimand J, Lan X, Head R, Zhu X, Kushida M, et al. Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity. Proc Natl Acad Sci USA. 2015;112(3):851–6.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  116. 116.

    Olive KP, Jacobetz MA, Davidson CJ, Gopinathan A, McIntyre D, Honess D, et al. Inhibition of Hedgehog signaling enhances delivery of chemotherapy in a mouse model of pancreatic cancer. Science. 2009;324(5933):1457–61.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  117. 117.

    Provenzano PP, Cuevas C, Chang AE, Goel VK, Von Hoff DD, Hingorani SR. Enzymatic targeting of the stroma ablates physical barriers to treatment of pancreatic ductal adenocarcinoma. Cancer Cell. 2012;21(3):418–29.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  118. 118.

    Jacobetz MA, Chan DS, Neesse A, Bapiro TE, Cook N, Frese KK, et al. Hyaluronan impairs vascular function and drug delivery in a mouse model of pancreatic cancer. Gut. 2013;62(1):112–20.

    CAS  PubMed  Article  Google Scholar 

  119. 119.

    Laklai H, Miroshnikova YA, Pickup MW, Collisson EA, Kim GE, Barrett AS, et al. Genotype tunes pancreatic ductal adenocarcinoma tissue tension to induce matricellular fibrosis and tumor progression. Nat Med. 2016;22(5):497–505.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  120. 120.

    Olivares O, Mayers JR, Gouirand V, Torrence ME, Gicquel T, Borge L, et al. Collagen-derived proline promotes pancreatic ductal adenocarcinoma cell survival under nutrient limited conditions. Nat Commun. 2017;8:16031.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  121. 121.

    Rice AJ, Cortes E, Lachowski D, Cheung BCH, Karim SA, Morton JP, et al. Matrix stiffness induces epithelial-mesenchymal transition and promotes chemoresistance in pancreatic cancer cells. Oncogenesis. 2017. https://doi.org/10.1038/oncsis.2017.54.

    Article  PubMed  PubMed Central  Google Scholar 

  122. 122.

    Peran I, Dakshanamurthy S, McCoy MD, Mavropoulos A, Allo B, Sebastian A, et al. Cadherin 11 promotes immunosuppression and extracellular matrix deposition to support growth of pancreatic tumors and resistance to gemcitabine in mice. Gastroenterology. 2021. https://doi.org/10.1053/j.gastro.2020.11.044.

    Article  PubMed  Google Scholar 

  123. 123.

    Erez N, Truitt M, Olson P, Hanahan D. Cancer-associated fibroblasts are activated in incipient neoplasia to orchestrate tumor-promoting inflammation in an NF-κB-dependent manner. Cancer Cell. 2010;17(2):135–47.

    CAS  PubMed  Article  Google Scholar 

  124. 124.

    Tjomsland V, Niklasson L, Sandström P, Borch K, Druid H, Bratthäll C, et al. The desmoplastic stroma plays an essential role in the accumulation and modulation of infiltrated immune cells in pancreatic adenocarcinoma. Clin Dev Immunol. 2011. https://doi.org/10.1155/2011/212810.

    Article  PubMed  PubMed Central  Google Scholar 

  125. 125.

    Feig C, Jones JO, Kraman M, Wells RJB, Deonarine A, Chan DS, et al. Targeting CXCL12 from FAP-expressing carcinoma-associated fibroblasts synergizes with anti-PD-L1 immunotherapy in pancreatic cancer. Proc Natl Acad Sci USA. 2013;110(50):20212–7.

    CAS  PubMed  Article  Google Scholar 

  126. 126.

    Zhang Y, Yan W, Collins MA, Bednar F, Rakshit S, Zetter BR, et al. Interleukin-6 is required for pancreatic cancer progression by promoting MAPK signaling activation and oxidative stress resistance. Cancer Res. 2013;73(20):6359–74.

    CAS  PubMed  Article  Google Scholar 

  127. 127.

    Sherman MH, Yu RT, Engle DD, Ding N, Atkins AR, Tiriac H, et al. Vitamin D receptor-mediated stromal reprogramming suppresses pancreatitis and enhances pancreatic cancer therapy. Cell. 2014;159(1):80–93.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  128. 128.

    Öhlund D, Handly-Santana A, Biffi G, Elyada E, Almeida AS, Ponz-Sarvise M, et al. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer. J Exp Med. 2017;214(3):579–96.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  129. 129.

    McAndrews KM, Chen Y, Kalluri R. Stromal cells exhibit prevalent genetic aberrations in colorectal cancer. Cancer Cell. 2020;38(6):774–5.

    CAS  PubMed  Article  Google Scholar 

  130. 130.

    Massalha H, Bahar Halpern K, Abu-Gazala S, Jana T, Massasa EE, Moor AE, et al. A single cell atlas of the human liver tumor microenvironment. Mol Syst Biol. 2020;16(12):e9682.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  131. 131.

    Zhang H, Zhou Y, Cheng Q, Dai Z, Wang Z, Liu F, et al. PDIA3 correlates with clinical malignant features and immune signature in human gliomas. Aging (Albany NY). 2020;12(15):15392–413.

    CAS  Article  Google Scholar 

  132. 132.

    Zhang L, Zhang Z. Recharacterizing tumor-infiltrating lymphocytes by single-cell RNA sequencing. Cancer Immunol Res. 2019;7(7):1040–6.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  133. 133.

    Aoki T, Chong LC, Takata K, Milne K, Hav M, Colombo A, et al. Single-cell transcriptome analysis reveals disease-defining T-cell subsets in the tumor microenvironment of classic hodgkin lymphoma. Cancer Discov. 2020;10(3):406–21.

    PubMed  Article  PubMed Central  Google Scholar 

  134. 134.

    Fisher DAC, Oh ST. Unraveling the architecture of classic hodgkin lymphoma one cell at a time. Cancer Discov. 2020;10(3):342–4.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  135. 135.

    Guo X, Zhang Y, Zheng L, Zheng C, Song J, Zhang Q, et al. Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat Med. 2018;24(7):978–85.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  136. 136.

    Song G, Shi Y, Zhang M, Goswami S, Afridi S, Meng L, et al. Global immune characterization of HBV/HCV-related hepatocellular carcinoma identifies macrophage and T-cell subsets associated with disease progression. Cell Discov. 2020;6(1):90.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  137. 137.

    Sun Y, Wu L, Zhong Y, Zhou K, Hou Y, Wang Z, et al. Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma. Cell. 2021. https://doi.org/10.1016/j.cell.2020.11.041.

    Article  PubMed  PubMed Central  Google Scholar 

  138. 138.

    Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. Cell PhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc. 2020;15(4):1484–506.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  139. 139.

    Cabello-Aguilar S, Alame M, Kon-Sun-Tack F, Fau C, Lacroix M, Colinge J. SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res. 2020;48(10):e55.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  140. 140.

    Yu X, Zhang L, Chaudhry A, Rapaport AS, Ouyang W. Unravelling the heterogeneity and dynamic relationships of tumor-infiltrating T cells by single-cell RNA sequencing analysis. J Leukoc Biol. 2020;107(6):917–32.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  141. 141.

    Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372(26):2509–20.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  142. 142.

    Zheng B, Wang D, Qiu X, Luo G, Wu T, Yang S, et al. Trajectory and functional analysis of PD-1(high) CD4(+)CD8(+) T cells in hepatocellular carcinoma by single-cell cytometry and transcriptome sequencing. Adv Sci (Weinh). 2020;7(13):2000224.

    CAS  Article  Google Scholar 

  143. 143.

    Zheng C, Zheng L, Yoo JK, Guo H, Zhang Y, Guo X, et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell. 2017;169(7):1342–56.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  144. 144.

    Wu TD, Madireddi S, de Almeida PE, Banchereau R, Chen YJ, Chitre AS, et al. Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature. 2020;579(7798):274–8.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  145. 145.

    Andor N, Simonds EF, Czerwinski DK, Chen J, Grimes SM, Wood-Bouwens C, et al. Single-cell RNA-Seq of follicular lymphoma reveals malignant B-cell types and coexpression of T-cell immune checkpoints. Blood. 2019;133(10):1119–29.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  146. 146.

    Milpied P, Cervera-Marzal I, Mollichella ML, Tesson B, Brisou G, Traverse-Glehen A, et al. Human germinal center transcriptional programs are de-synchronized in B cell lymphoma. Nat Immunol. 2018;19(9):1013–24.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  147. 147.

    Roider T, Seufert J, Uvarovskii A, Frauhammer F, Bordas M, Abedpour N, et al. Dissecting intratumour heterogeneity of nodal B-cell lymphomas at the transcriptional, genetic and drug-response levels. Nat Cell Biol. 2020;22(7):896–906.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  148. 148.

    Jaitin DA, Weiner A, Yofe I, Lara-Astiaso D, Keren-Shaul H, David E, et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-Seq. Cell. 2016;167(7):1883–96.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  149. 149.

    Pombo Antunes AR, Scheyltjens I, Lodi F, Messiaen J, Antoranz A, Duerinck J, et al. Single-cell profiling of myeloid cells in glioblastoma across species and disease stage reveals macrophage competition and specialization. Nat Neurosci. 2021;24(4):595–610.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  150. 150.

    Hubert M, Gobbini E, Bendriss-Vermare N, Caux C, Valladeau-Guilemond J. Human tumor-infiltrating dendritic cells: from in situ visualization to high-dimensional analyses. Cancers (Basel). 2019;11(8):1082.

    CAS  PubMed Central  Article  Google Scholar 

  151. 151.

    Gohil SH, Iorgulescu JB, Braun DA, Keskin DB, Livak KJ. Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy. Nat Rev Clin Oncol. 2021. https://doi.org/10.1038/s41571-020-00449-x.

    Article  PubMed  PubMed Central  Google Scholar 

  152. 152.

    Prieto J, Melero I, Sangro B. Immunological landscape and immunotherapy of hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol. 2015;12(12):681–700.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  153. 153.

    Brooks ED, Chang JY. Time to abandon single-site irradiation for inducing abscopal effects. Nat Rev Clin Oncol. 2019;16(2):123–35.

    PubMed  Article  Google Scholar 

  154. 154.

    Hu J, Chen Z, Bao L, Zhou L, Hou Y, Liu L, et al. Single-cell transcriptome analysis reveals intratumoral heterogeneity in ccRCC, which results in different clinical outcomes. Mol Ther. 2020;28(7):1658–72.

    CAS  PubMed  Article  Google Scholar 

  155. 155.

    Magen A, Nie J, Ciucci T, Tamoutounour S, Zhao Y, Mehta M, et al. Single-cell profiling defines transcriptomic signatures specific to tumor-reactive versus virus-responsive CD4(+) T cells. Cell Rep. 2019;29(10):3019–32.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  156. 156.

    Kato Y, Tabata K, Kimura T, Yachie-Kinoshita A, Ozawa Y, Yamada K, et al. Lenvatinib plus anti-PD-1 antibody combination treatment activates CD8+ T cells through reduction of tumor-associated macrophage and activation of the interferon pathway. PLoS ONE. 2019;14(2):e0212513.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  157. 157.

    Katsuta E, Qi QY, Peng X, Hochwald SN, Yan L, Takabe K. Pancreatic adenocarcinomas with mature blood vessels have better overall survival. Sci Rep. 2019;9(1):1310.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  158. 158.

    Goswami S, Walle T, Cornish AE, Basu S, Anandhan S, Fernandez I, et al. Immune profiling of human tumors identifies CD73 as a combinatorial target in glioblastoma. Nat Med. 2020;26(1):39–46.

    CAS  PubMed  Article  Google Scholar 

  159. 159.

    Suzuki A, Matsushima K, Makinoshima H, Sugano S, Kohno T, Tsuchihara K, et al. Single-cell analysis of lung adenocarcinoma cell lines reveals diverse expression patterns of individual cells invoked by a molecular target drug treatment. Genome Biol. 2015;16:66.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  160. 160.

    Su X, Long Q, Bo J, Shi Y, Zhao LN, Lin Y, et al. Mutational and transcriptomic landscapes of a rare human prostate basal cell carcinoma. Prostate. 2020;80(6):508–17.

    CAS  PubMed  Article  Google Scholar 

  161. 161.

    Sa JK, Choi SW, Zhao J, Lee Y, Zhang J, Kong DS, et al. Hypermutagenesis in untreated adult gliomas due to inherited mismatch mutations. Int J Cancer. 2019;144(12):3023–30.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  162. 162.

    Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ, Melms JC, et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell. 2018;175(4):984–97.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  163. 163.

    Hong SP, Chan TE, Lombardo Y, Corleone G, Rotmensz N, Bravaccini S, et al. Single-cell transcriptomics reveals multi-step adaptations to endocrine therapy. Nat Commun. 2019;10(1):3840.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  164. 164.

    Gurjao C, Liu D, Hofree M, AlDubayan SH, Wakiro I, Su MJ, et al. Intrinsic resistance to immune checkpoint blockade in a mismatch repair-deficient colorectal cancer. Cancer Immunol Res. 2019;7(8):1230–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  165. 165.

    Rendeiro AF, Krausgruber T, Fortelny N, Zhao F, Penz T, Farlik M, et al. Chromatin mapping and single-cell immune profiling define the temporal dynamics of ibrutinib response in CLL. Nat Commun. 2020;11(1):577.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  166. 166.

    Herman SE, Mustafa RZ, Gyamfi JA, Pittaluga S, Chang S, Chang B, et al. Ibrutinib inhibits BCR and NF-kappaB signaling and reduces tumor proliferation in tissue-resident cells of patients with CLL. Blood. 2014;123(21):3286–95.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  167. 167.

    Long M, Beckwith K, Do P, Mundy BL, Gordon A, Lehman AM, et al. Ibrutinib treatment improves T cell number and function in CLL patients. J Clin Investig. 2017;127(8):3052–64.

    PubMed  Article  PubMed Central  Google Scholar 

  168. 168.

    Hinohara K, Wu HJ, Vigneau S, McDonald TO, Igarashi KJ, Yamamoto KN, et al. KDM5 histone demethylase activity links cellular transcriptomic heterogeneity to therapeutic resistance. Cancer Cell. 2018;34(6):939–53.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  169. 169.

    Kim JM, Chen DS. Immune escape to PD-L1/PD-1 blockade: seven steps to success (or failure). Ann Oncol. 2016;27(8):1492–504.

    CAS  PubMed  Article  Google Scholar 

  170. 170.

    Whitaker RM, Gillaspie EA, Horn L, Osmundson EC, et al. Update of retrospective analysis of the interaction of immune-checkpoint blockade and palliative radiotherapy on clinical outcomes in patients with stage IV non-small cell lung cancer (NSCLC). Int J Radiat Oncol. 2020. https://doi.org/10.1016/j.ijrobp.2020.07.2218.

    Article  Google Scholar 

  171. 171.

    Jang BS, Han W, Kim IA. Tumor mutation burden, immune checkpoint crosstalk and radiosensitivity in single-cell RNA sequencing data of breast cancer. Radiother Oncol. 2020;142:202–9.

    CAS  PubMed  Article  Google Scholar 

  172. 172.

    Wang J, Xu R, Yuan H, Zhang Y, Cheng S. Single-cell RNA sequencing reveals novel gene expression signatures of trastuzumab treatment in HER2+ breast cancer: a pilot study. Medicine (Baltimore). 2019;98(26):e15872.

    Article  Google Scholar 

  173. 173.

    Zhang J, Guan M, Wang Q, Zhang J, Zhou T, Sun X. Single-cell transcriptome-based multilayer network biomarker for predicting prognosis and therapeutic response of gliomas. Brief Bioinform. 2020;21(3):1080–97.

    CAS  PubMed  Article  Google Scholar 

  174. 174.

    Chen Z, Zhou L, Liu L, Hou Y, Xiong M, Yang Y, et al. Single-cell RNA sequencing highlights the role of inflammatory cancer-associated fibroblasts in bladder urothelial carcinoma. Nat Commun. 2020;11(1):5077.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  175. 175.

    Robertson AG, Kim J, Al-Ahmadie H, Bellmunt J, Guo G, Cherniack AD, et al. Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell. 2017. https://doi.org/10.1016/j.cell.2017.09.007.

    Article  PubMed  PubMed Central  Google Scholar 

  176. 176.

    Yang L, Zhang X, Hou Q, Huang M, Zhang H, Jiang Z, et al. Single-cell RNA-seq of esophageal squamous cell carcinoma cell line with fractionated irradiation reveals radioresistant gene expression patterns. BMC Genom. 2019;20(1):611.

    Article  CAS  Google Scholar 

  177. 177.

    Srivatsan SR, McFaline-Figueroa JL, Ramani V, Saunders L, Cao J, Packer J, et al. Massively multiplex chemical transcriptomics at single-cell resolution. Science. 2020;367(6473):45–51.

    CAS  PubMed  Article  Google Scholar 

  178. 178.

    Yachida S, Jones S, Bozic I, Antal T, Leary R, Fu B, et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature. 2010;467(7319):1114–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  179. 179.

    Tewari KS, Sill MW, Penson RT, Huang H, Ramondetta LM, Landrum LM, et al. Bevacizumab for advanced cervical cancer: final overall survival and adverse event analysis of a randomised, controlled, open-label, phase 3 trial (Gynecologic Oncology Group 240). The Lancet. 2017;390(10103):1654–63.

    CAS  Article  Google Scholar 

  180. 180.

    Nguyen DX, Bos PD, Massagué J. Metastasis: from dissemination to organ-specific colonization. Nat Rev Cancer. 2009;9(4):274–84.

    CAS  PubMed  Article  Google Scholar 

  181. 181.

    Vasudev NS, Reynolds AR. Anti-angiogenic therapy for cancer: current progress, unresolved questions and future directions. Angiogenesis. 2014;17(3):471–94.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  182. 182.

    Daneshmanesh AH, Hojjat-Farsangi M, Ghaderi A, Moshfegh A, Hansson L, Schultz J, et al. A receptor tyrosine kinase ROR1 inhibitor (KAN0439834) induced significant apoptosis of pancreatic cells which was enhanced by erlotinib and ibrutinib. PLoS ONE. 2018;13(6):e0198038.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  183. 183.

    Chang Y, Li G, Zhai Y, Huang L, Feng Y, Wang D, et al. Redox regulator GLRX is associated with tumor immunity in glioma. Front Immunol. 2020;11:580934.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  184. 184.

    Tanaka N, Katayama S, Reddy A, Nishimura K, Niwa N, Hongo H, et al. Single-cell RNA-seq analysis reveals the platinum resistance gene COX7B and the surrogate marker CD63. Cancer Med. 2018;7(12):6193–204.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  185. 185.

    L’Esperance S, Popa I, Bachvarova M, Plante M, Patten N, Wu L, et al. Gene expression profiling of paired ovarian tumors obtained prior to and following adjuvant chemotherapy: molecular signatures of chemoresistant tumors. Int J Oncol. 2006;29(1):5–24.

    CAS  PubMed  Google Scholar 

  186. 186.

    Hannemann J, Oosterkamp HM, Bosch CA, Velds A, Wessels LF, Loo C, et al. Changes in gene expression associated with response to neoadjuvant chemotherapy in breast cancer. J Clin Oncol. 2005;23(15):3331–42.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  187. 187.

    Sun YM, Chen YQ. Principles and innovative technologies for decrypting noncoding RNAs: from discovery and functional prediction to clinical application. J Hematol Oncol. 2020;13(1):109.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  188. 188.

    Wan Q, Liu C, Liu C, Liu W, Wang X, Wang Z. Discovery and validation of a metastasis-related prognostic and diagnostic biomarker for melanoma based on single cell and gene expression datasets. Front Oncol. 2020;10:585980.

    PubMed  PubMed Central  Article  Google Scholar 

  189. 189.

    Chubb JR, Trcek T, Shenoy SM, Singer RH. Transcriptional pulsing of a developmental gene. Curr Biol. 2006;16(10):1018–25.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  190. 190.

    Dar RD, Razooky BS, Singh A, Trimeloni TV, McCollum JM, Cox CD, et al. Transcriptional burst frequency and burst size are equally modulated across the human genome. Proc Natl Acad Sci USA. 2012;109(43):17454–9.

    CAS  PubMed  Article  Google Scholar 

  191. 191.

    Gierahn TM, Wadsworth MH 2nd, Hughes TK, Bryson BD, Butler A, Satija R, et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods. 2017;14(4):395–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  192. 192.

    Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015;161(5):1187–201.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  193. 193.

    Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161(5):1202–14.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  194. 194.

    Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014;343(6172):776–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  195. 195.

    Hashimshony T, Senderovich N, Avital G, Klochendler A, de Leeuw Y, Anavy L, et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 2016. https://doi.org/10.1186/s13059-016-0938-8.

    Article  PubMed  PubMed Central  Google Scholar 

  196. 196.

    Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10(11):1096–8.

    CAS  PubMed  Article  Google Scholar 

  197. 197.

    Wang Q, Xiong H, Ai S, Yu X, Liu Y, Zhang J, et al. CoBATCH for high-throughput single-cell epigenomic profiling. Mol Cell. 2019;76(1):206–16.

    CAS  PubMed  Article  Google Scholar 

  198. 198.

    Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015;523(7561):486–90.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  199. 199.

    Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017;14(9):865–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  200. 200.

    Peterson VM, Zhang KX, Kumar N, Wong J, Li L, Wilson DC, et al. Multiplexed quantification of proteins and transcripts in single cells. Nat Biotechnol. 2017;35(10):936–9.

    CAS  PubMed  Article  Google Scholar 

  201. 201.

    Sinjab A, Han G, Treekitkarnmongkol W, Hara K, Brennan PM, Dang M, et al. Resolving the spatial and cellular architecture of lung adenocarcinoma by multiregion single-cell sequencing. Cancer Discov. 2021. https://doi.org/10.1158/2159-8290.Cd-20-1285.

    Article  PubMed  Google Scholar 

  202. 202.

    Pal B, Chen Y, Vaillant F, Capaldo BD, Joyce R, Song X, et al. A single-cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast. Embo J. 2021. https://doi.org/10.15252/embj.2020107333.

    Article  PubMed  PubMed Central  Google Scholar 

  203. 203.

    Wagner J, Rapsomaniki MA, Chevrier S, Anzeneder T, Langwieder C, Dykgers A, et al. A single-cell atlas of the tumor and immune ecosystem of human breast cancer. Cell. 2019;177(5):1330–45.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  204. 204.

    Hu Q, Hong Y, Qi P, Lu G, Mai X, Xu S, et al. Atlas of breast cancer infiltrated B-lymphocytes revealed by paired single-cell RNA-sequencing and antigen receptor profiling. Nat Commun. 2021;12(1):2186.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  205. 205.

    Mathewson ND, Ashenberg O, Tirosh I, Gritsch S, Perez EM, Marx S, et al. Inhibitory CD161 receptor identified in glioma-infiltrating T cells by single-cell analysis. Cell. 2021;184(5):1281–98.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  206. 206.

    Zheng Y, Chen Z, Han Y, Han L, Zou X, Zhou B, et al. Immune suppressive landscape in the human esophageal squamous cell carcinoma microenvironment. Nat Commun. 2020;11(1):6268.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  207. 207.

    Yuan H, Yan M, Zhang G, Liu W, Deng C, Liao G, et al. CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res. 2019. https://doi.org/10.1093/nar/gky939.

    Article  PubMed  PubMed Central  Google Scholar 

  208. 208.

    Pritykin Y, van der Veeken J, Pine AR, Zhong Y, Sahin M, Mazutis L, et al. A unified atlas of CD8 T cell dysfunctional states in cancer and infection. Mol Cell. 2021. https://doi.org/10.1016/j.molcel.2021.03.045.

    Article  PubMed  Google Scholar 

  209. 209.

    Cheng S, Li Z, Gao R, Xing B, Gao Y, Yang Y, et al. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell. 2021;184(3):792–809.

    CAS  PubMed  Article  Google Scholar 

  210. 210.

    Marcu A, Bichmann L, Kuchenbecker L, Kowalewski DJ, Freudenmann LK, Backert L, et al. HLA Ligand Atlas: a benign reference of HLA-presented peptides to improve T-cell-based cancer immunotherapy. J Immunother Cancer. 2021. https://doi.org/10.1136/jitc-2020-002071.

    Article  PubMed  PubMed Central  Google Scholar 

  211. 211.

    Buechler MB, Pradhan RN, Krishnamurty AT, Cox C, Calviello AK, Wang AW, et al. Cross-tissue organization of the fibroblast lineage. Nature. 2021. https://doi.org/10.1038/s41586-021-03549-5.

    Article  PubMed  Google Scholar 

  212. 212.

    Schlesinger Y, Yosefov-Levi O, Kolodkin-Gal D, Granit RZ, Peters L, Kalifa R, et al. Single-cell transcriptomes of pancreatic preinvasive lesions and cancer reveal acinar metaplastic cells’ heterogeneity. Nat Commun. 2020;11(1):4516.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  213. 213.

    Dong Y, Wang Z, Shi Q. Liquid biopsy based single-cell transcriptome profiling characterizes heterogeneity of disseminated tumor cells from lung adenocarcinoma. Proteomics. 2020;20(13):e1900224.

    PubMed  Article  CAS  Google Scholar 

  214. 214.

    Taverna JA, Hung CN, DeArmond DT, Chen M, Lin CL, Osmulski PA, et al. Single-cell proteomic profiling identifies combined AXL and JAK1 inhibition as a novel therapeutic strategy for lung cancer. Cancer Res. 2020;80(7):1551–63.

    CAS  PubMed  PubMed Central  Google Scholar 

  215. 215.

    Lu T, Yang X, Shi Y, Zhao M, Bi G, Liang J, et al. Single-cell transcriptome atlas of lung adenocarcinoma featured with ground glass nodules. Cell Discov. 2020;6:69.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  216. 216.

    Zhang P, Yang M, Zhang Y, Xiao S, Lai X, Tan A, et al. Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer. Cell Rep. 2019;27(6):1934–47.

    CAS  PubMed  Article  Google Scholar 

  217. 217.

    Jiang L, Li XP, Dai YT, Chen B, Weng XQ, Xiong SM, et al. Multidimensional study of the heterogeneity of leukemia cells in t(8;21) acute myelogenous leukemia identifies the subtype with poor outcome. P Natl Acad Sci USA. 2020;117(33):20117–26.

    CAS  Article  Google Scholar 

  218. 218.

    Jang JS, Li Y, Mitra AK, Bi L, Abyzov A, van Wijnen AJ, et al. Molecular signatures of multiple myeloma progression through single cell RNA-Seq. Blood Cancer J. 2019;9(1):2.

    PubMed  PubMed Central  Article  Google Scholar 

  219. 219.

    Dong B, Miao J, Wang Y, Luo W, Ji Z, Lai H, et al. Single-cell analysis supports a luminal-neuroendocrine transdifferentiation in human prostate cancer. Commun Biol. 2020;3(1):778.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  220. 220.

    Zhang L, He X, Liu X, Zhang F, Huang LF, Potter AS, et al. Single-cell transcriptomics in medulloblastoma reveals tumor-initiating progenitors and oncogenic cascades during tumorigenesis and relapse. Cancer Cell. 2019;36(3):302–18.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  221. 221.

    Weng Q, Wang J, Wang J, He D, Cheng Z, Zhang F, et al. Single-cell transcriptomics uncovers glial progenitor diversity and cell fate determinants during development and gliomagenesis. Cell Stem Cell. 2019;24(5):707–23.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  222. 222.

    Wang J, Liu J, Sun G, Meng H, Wang J, Guan Y, et al. Glioblastoma extracellular vesicles induce the tumour-promoting transformation of neural stem cells. Cancer Lett. 2019;466:1–12.

    CAS  PubMed  Article  Google Scholar 

  223. 223.

    Wang Q, Tan Y, Fang C, Zhou J, Wang Y, Zhao K, et al. Single-cell RNA-seq reveals RAD51AP1 as a potent mediator of EGFRvIII in human glioblastomas. Aging (Albany NY). 2019;11(18):7707–22.

    CAS  Article  Google Scholar 

  224. 224.

    Wu H, Yu J, Kong D, Xu Y, Zhang Z, Shui J, et al. Population and singlecell transcriptome analyses reveal diverse transcriptional changes associated with radioresistance in esophageal squamous cell carcinoma. Int J Oncol. 2019. https://doi.org/10.3892/ijo.2019.4897.

    Article  PubMed  PubMed Central  Google Scholar 

  225. 225.

    Yan P, Zhou B, Ma Y, Wang A, Hu X, Luo Y, et al. Tracking the important role of JUNB in hepatocellular carcinoma by single-cell sequencing analysis. Oncol Lett. 2019. https://doi.org/10.3892/ol.2019.11235.

    Article  PubMed  PubMed Central  Google Scholar 

  226. 226.

    Lu Q, Gao J, Tang S, Li Z, Wang X, Deng C, et al. Integrated RNA sequencing and single-cell mass cytometry reveal a novel role of LncRNA HOXA-AS2 in tumorigenesis and stemness of hepatocellular carcinoma. Onco Targets Ther. 2020;13:10901–16.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  227. 227.

    Liu J, Adhav R, Miao K, Su SM, Mo L, Chan UI, et al. Characterization of BRCA1-deficient premalignant tissues and cancers identifies Plekha5 as a tumor metastasis suppressor. Nat Commun. 2020;11(1):4875.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  228. 228.

    Peng L, Chen Y, Ou Q, Wang X, Tang N. LncRNA MIAT correlates with immune infiltrates and drug reactions in hepatocellular carcinoma. Int Immunopharmacol. 2020. https://doi.org/10.1016/j.intimp.2020.107071.

    Article  PubMed  Google Scholar 

  229. 229.

    Gaydosik AM, Tabib T, Geskin LJ, Bayan CA, Conway JF, Lafyatis R, et al. Single-cell lymphocyte heterogeneity in advanced cutaneous T-cell lymphoma skin tumors. Clin Cancer Res. 2019;25(14):4443–54.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  230. 230.

    Borcherding N, Voigt AP, Liu V, Link BK, Zhang W, Jabbari A. Single-cell profiling of cutaneous T-cell lymphoma reveals underlying heterogeneity associated with disease progression. Clin Cancer Res. 2019;25(10):2996–3005.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  231. 231.

    Alshetaiwi H, Pervolarakis N, Nguyen QH, Kessenbrock K. Single-cell transcriptome analysis workflow for splenic myeloid-derived suppressor cells from murine breast cancer models. Methods Mol Biol. 2021;2236:177–87.

    PubMed  Article  Google Scholar 

  232. 232.

    Wang D, Prager BC, Gimple RC, Aguilar B, Alizadeh D, Tang H, et al. CRISPR screening of CAR T cells and cancer stem cells reveals critical dependencies for cell-based therapies. Cancer Discov. 2021. https://doi.org/10.1158/2159-8290.Cd-20-1243.

    Article  PubMed  PubMed Central  Google Scholar 

  233. 233.

    Ryu D, Kim SJ, Hong Y, Jo A, Kim N, Kim HJ, et al. Alterations in the transcriptional programs of myeloma cells and the microenvironment during extramedullary progression affect proliferation and immune evasion. Clin Cancer Res. 2020;26(4):935–44.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  234. 234.

    Zhang Y, Song J, Zhao Z, Yang M, Chen M, Liu C, et al. Single-cell transcriptome analysis reveals tumor immune microenvironment heterogenicity and granulocytes enrichment in colorectal cancer liver metastases. Cancer Lett. 2020;470:84–94.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  235. 235.

    Sheih A, Voillet V, Hanafi LA, DeBerg HA, Yajima M, Hawkins R, et al. Clonal kinetics and single-cell transcriptional profiling of CAR-T cells in patients undergoing CD19 CAR-T immunotherapy. Nat Commun. 2020;11(1):219.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We thank American Journal Experts (AJE) for their assistance with language editing.

Funding

This study was funded by the National Natural Science Foundation of China (No. 81802352, 81772555 and 81902428), the National Science Foundation for Distinguished Young Scholars of China (No. 81625016), the Shanghai Sailing Program (No. 19YF1409400 and 20YF1409000), the Shanghai Rising-Star Program (No. 20QA1402100), the Shanghai Anticancer Association Young Eagle Program (No. SACA-CY19A06), the Clinical and Scientific Innovation Project of Shanghai Hospital Development Center (No. SHDC12018109 and SHDC12019109), the Scientific Innovation Project of Shanghai Education Committee (No. 2019-01-07-00-07-E00057) and the Fudan University Shanghai Medical College Qing Feng Scholar Project (No. QF2110).

Author information

Affiliations

Authors

Contributions

YLL and TR collected the related papers and wrote the manuscript. JX, WW, BZ and JL participated in the design of the review, and SS and XJY initiated the study and revised and finalized the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xianjun Yu or Si Shi.

Ethics declarations

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lei, Y., Tang, R., Xu, J. et al. Applications of single-cell sequencing in cancer research: progress and perspectives. J Hematol Oncol 14, 91 (2021). https://doi.org/10.1186/s13045-021-01105-2

Download citation

Keywords

  • Single-cell sequencing
  • Heterogeneity
  • Microenvironment
  • Circulating tumor cells