Transcriptional Heterogeneity Research Articles

EPCO-03. A COMPREHENSIVE SINGLE-CELL ATLAS OF SCHWANNOMA REVEALS CONSERVED CELLULAR STATES ACROSS DIFFERENT GENETIC BACKGROUNDS AND ANATOMIC LOCATIONS

Abstract BACKGROUND Despite being associated with molecular alterations in the NF2 gene, there is limited understanding of the gene expression programs that govern the behavior of schwannomas both in sporadic cases as well as in patients with schwannoma predisposition syndromes, and also with respect to their anatomic location. METHODS We characterized the transcriptional heterogeneity of schwannomas across different genetic backgrounds and anatomic locations at the single-cell level. Tumor samples from patients undergoing resection for symptomatic schwannoma were dissociated and single cells were processed for full-transcript whole transcriptome profiling via Smart-seq2. Our cohort of 22 schwannomas, includes 5 tumors from schwannomatosis patients, 11 tumors from neurofibromatosis type 2 patients, and 6 sporadic tumors – 3 of which had grown after radiation treatment. Anatomically, 12 tumors were excised from cranial nerves (11 vestibular, 1 accessory) and 10 from spinal and other peripheral nerves. RESULTS We profiled 11,373 cells, including 4725 neoplastic cells, 2425 fibroblasts, 1314 T cells, 1258 endothelial cells, 969 myeloid cells, 503 pericytes and 240 Schwann cells. Copy number alteration analyses reveal evidence of intratumoral polyclonality (supporting the hypothesis of multiple sites of origin for a tumor). Using non-negative matrix factorization, we identify 6 distinct transcriptional metaprograms, with gene signatures related to stress, myelin production, antigen presentation, interferon signaling, glycolysis and extracellular matrix. After comparison with a pan-cancer gene expression dataset, we note unique interferon and antigen presentation metaprograms in schwannoma, with the latter being mainly characterized by expression of MHC Class II genes. Overall, we observe conserved expression of these metaprograms across anatomic locations and genetic backgrounds, with the exception of decreased interferon signaling for vestibular tumors. CONCLUSION We provide a comprehensive single-cell characterization of schwannoma spanning diverse genetic and anatomic backgrounds. We describe unique transcriptional metaprograms for antigen presentation and interferon signaling, which might underlie mechanisms for immune evasion.

Metronomic and single high-dose paclitaxel treatments produce distinct heterogenous chemoresistant cancer cell populations

More than 75% of epithelial ovarian cancer (EOC) patients experience disease recurrence after initial treatment, highlighting our incomplete understanding of how chemoresistant populations evolve over the course of EOC progression post chemotherapy treatment. Here, we show how two paclitaxel (PTX) treatment methods- a single high dose and a weekly metronomic dose for four weeks, generate unique chemoresistant populations. Using mechanically relevant alginate microspheres and a combination of transcript profiling and heterogeneity analyses, we found that these PTX-treatment regimens produce distinct and resilient subpopulations that differ in metabolic reprogramming signatures, acquisition of resistance to PTX and anoikis, and the enrichment for cancer stem cells (CSCs) and polyploid giant cancer cells (PGCCs) with the ability to replenish bulk populations. We investigated the longevity of these metabolic reprogramming events using untargeted metabolomics and found that metabolites associated with stemness and therapy-induced senescence were uniquely abundant in populations enriched for CSCs and PGCCs. Predictive network analysis revealed that antioxidative mechanisms were likely to be differentially active dependent on both time and exposure to PTX. Our results illustrate how current standard chemotherapies contribute to the development of chemoresistant EOC subpopulations by either selecting for intrinsically resistant subpopulations or promoting the evolution of resistance mechanisms. Additionally, our work describes the unique phenotypic signatures in each of these distinct resistant subpopulations and thus highlights potential vulnerabilities that can be exploited for more effective treatment.

Genetic diversity, transcript heterogeneity and allele mining of TaSKP1-6B-4 gene variants across diverse genotypes under terminal heat stress and genome wide characterization of TaSKP1 gene family from bread wheat (Triticum aestivum L.).

SKP1 (S-phase kinase protein1) is an essential regulatory component of SCF (Skp1-cullin-F-box) E3 ubiquitin ligases involved in maintenance of cellular protein homeostasis through ubiquitin mediated proteasome system (UPS). UPS play a key role in stress response and grain yield. Earlier, we isolated TaSKP1-6B-4, highly induced in flag leaf tissues (Accession No. KJ830759.1) of developing wheat caryopses under heat stress. To further assess the functional role of SKP1, genetic variability analysis was carried out in a panel of 25 contrasting germplasm through extensive phenotyping and transcript profiling of TaSKP1-6B-4 during anthesis under ambient and terminal heat stress (THS) in field experiments for two consecutive years. The analysis of variance revealed significant variations for all the traits studied. Higher H2(%), GCV, PCV, GA and GA% mean observed in tiller number per plant (23.81, 17.65, 5.71, 28, 30.86%) and grain number per head (30.27, 82.79, 60.16, 105.00, 108.64%) under THS over ambient temperature. Higher fold induction of TaSKP1-6B-4 transcripts was recorded in 10 genotypes viz. HD2967 (9.9), IC145456 (6.18) in flag leaf; while C-306 (15.88), RAJ3765 (8.37) in ear head. Allele mining of SKP1-6B-4 showed genotypic sequence variations. Whole genome wide search of SKP1 gene family identified 95 SKP1 genes which were structurally characterized. Grain yield, leaf senescence and other agronomic-morpho-physiological parameters combined with transcript profiling, cvHD2967, was found to be the best positively responsive to THS which by pedigree was not heat tolerant. We report a novel 2 year comprehensive field based analysis on collective genetic variability and SKP1/UPS modulation under a natural environmental setting. The data reveals potential functional role of UPS under THS and tolerant cultivars can be further utilized for clarifying the role of UPS mechanistically at the molecular level and for developing terminal heat stress tolerant wheat.

Bcl6 is a subset-defining transcription factor of lymphoid tissue inducer-like ILC3

Innate lymphoid cells (ILCs) are tissue-resident effector cells with roles in tissue homeostasis, protective immunity, and inflammatory disease. Group 3 ILCs (ILC3s) are classically defined by the master transcription factor RORγt. However, ILC3 can be further subdivided into subsets that share type 3 effector modules that exhibit significant ontological, transcriptional, phenotypic, and functional heterogeneity. Notably lymphoid tissue inducer (LTi)-like ILC3s mediate effector functions not typically associated with other RORγt-expressing lymphocytes, suggesting that additional transcription factors contribute to dictate ILC3 subset phenotypes. Here, we identify Bcl6 as a subset-defining transcription factor of LTi-like ILC3s in mice and humans. Deletion of Bcl6 results in dysregulation of the LTi-like ILC3 transcriptional program and markedly enhances expression of interleukin-17A (IL-17A) and IL-17F in LTi-like ILC3s in a manner in part dependent upon the commensal microbiota-and associated with worsened inflammation in a model of colitis. Together, these findings redefine our understanding of ILC3 subset biology.

Novel time-resolved reporter mouse reveals spatial and transcriptional heterogeneity during alpha cell differentiation.

Glucagon-expressing pancreatic alpha cells have attracted much attention for their plasticity to transdifferentiate into insulin-producing beta cells; however, it remains unclear precisely when, and from where, alpha cells emerge and what regulates alpha cell fate. We therefore explored the spatial and transcriptional heterogeneity of alpha cell differentiation using a novel time-resolved reporter system. We established the mouse model, 'Gcg-Timer', in which newly generated alpha cells can be distinguished from more-differentiated cells by their fluorescence. Fluorescence imaging and transcriptome analysis were performed with Gcg-Timer mice during the embryonic and postnatal stages. Fluorescence imaging and flow cytometry demonstrated that green fluorescence-dominant cells were present in Gcg-Timer mice at the embryonic and neonatal stages but not after 1 week of age, suggesting that alpha cell neogenesis occurs during embryogenesis and early neonatal stages under physiological conditions. Transcriptome analysis of Gcg-Timer embryos revealed that the mRNAs related to angiogenesis were enriched in newly generated alpha cells. Histological analysis revealed that some alpha cells arise close to the pancreatic ducts, whereas the others arise away from the ducts and adjacent to the blood vessels. Notably, when the glucagon signal was suppressed by genetic ablation or by chemicals, such as neutralising glucagon antibody, green-dominant cells emerged again in adult mice. Novel time-resolved analysis with Gcg-Timer reporter mice uncovered spatiotemporal features of alpha cell neogenesis that will enhance our understanding of cellular identity and plasticity within the islets. Raw and processed RNA sequencing data for this study has been deposited in the Gene Expression Omnibus under accession number GSE229090.

Single-Cell RNA Sequencing Analysis of the Early Postnatal Mouse Lens Epithelium.

The lens epithelium maintains the overall health of the organ. We used single-cell RNA sequencing (scRNA-seq) technology to assess transcriptional heterogeneity between cells in the postnatal day 2 (P2) epithelium and identify distinct epithelial cell subtypes. Analysis of these data was used to better understand lens growth, differentiation, and homeostasis on P2. scRNA-seq on P2 mouse lenses was performed using the 10x Genomics Chromium Single Cell 3' Kit (v3.1) and short-read Illumina sequencing. Sequence alignment and preprocessing of data were conducted using 10x Genomics Cell Ranger software. Seurat was employed for preprocessing, quality control, dimensionality reduction, and cell clustering, and Monocle was utilized for trajectory analysis to understand the developmental progression of the lens cells. CellChat and GO analyses were used to explore cell-cell communication networks and signaling interactions. Lens epithelial cells (LECs) were divided into seven subclusters, classified by specific gene markers. The expression of crystallin, cell-cycle, and metabolic genes was not uniform, indicating distinct functional roles of LECs. Trajectory analysis predicted a bifurcation of differentiating and cycling cells from an Igfbp5+ progenitor pool. We also identified heterogeneity in signaling molecules and pathways, suggesting that cycling and progenitor subclusters have prominent roles in coordinating crosstalk. scRNA-seq corroborated many known markers of epithelial differentiation and proliferation while providing further insight into the pathways and genes directing these processes. Interestingly, we demonstrated that the developing epithelium can be divided into distinct subpopulations. These clusters reflect the transcriptionally diverse roles of the epithelium in proliferation, signaling, and maintenance.

Genomic and Evolutionary Characterization of Concurrent Intraductal Carcinoma and Adenocarcinoma of the Prostate.

The genomic, transcriptomic, and epigenomic characterization of concurrent intraductal carcinoma and adenocarcinoma of the prostate deepens the biological understanding of this lethal disease and provides a genetic basis for developing targeted therapies.

Reduction of IFN-I responses by plasmacytoid dendritic cells in a longitudinal trans men cohort

Type I interferons (IFN-I) are important mediators of antiviral immunity and autoimmune diseases. Female plasmacytoid dendritic cells (pDCs) exert an elevated capacity to produce IFN-I upon toll-like receptor 7 (TLR7) activation compared to male pDCs, and both sex hormones and X-encoded genes have been implicated in these sex-specific differences. Using longitudinal samples from a trans men cohort receiving gender-affirming hormone therapy (GAHT), the impact of testosterone injections on TLR7-mediated IFN-I production by pDCs was assessed. Single-cell RNA analyses of pDCs showed downregulation of IFN-I-related gene expression signatures but also revealed transcriptional inter-donor heterogeneity. Longitudinal quantification showed continuous reduction of IFN-I protein production by pDCs and reduced expression of IFN-I-stimulated genes in peripheral blood mononuclear cells (PBMCs). These studies in trans men demonstrate that testosterone administration reduces IFN-I production by pDCs over time and provide insights into the immune-modulatory role of testosterone in sex-specific IFN-I-mediated immune responses.

Smurfness-based two-phase model of ageing helps deconvolve the ageing transcriptional signature.

Ageing is characterised at the molecular level by six transcriptional 'hallmarks of ageing', that are commonly described as progressively affected as time passes. By contrast, the 'Smurf' assay separates high-and-constant-mortality risk individuals from healthy, zero-mortality risk individuals, based on increased intestinal permeability. Performing whole body total RNA sequencing, we found that Smurfness distinguishes transcriptional changes associated with chronological age from those associated with biological age. We show that transcriptional heterogeneity increases with chronological age in non-Smurf individuals preceding the other five hallmarks of ageing that are specifically associated with the Smurf state. Using this approach, we also devise targeted pro-longevity genetic interventions delaying entry in the Smurf state. We anticipate that increased attention to the evolutionary conserved Smurf phenotype will bring about significant advances in our understanding of the mechanisms of ageing.

Feature-weight based measurement of cancerous transcriptome using cohort-wide and sample-specific information.

Identifying cancerous samples or cells using transcriptomic data is critical for cancer related basic research, early diagnosis, and targeted therapy. However, the high transcriptional heterogeneity of cancers still hinders people from accurately recognizing cancerous transcriptome using bulk, single-cell, or spatial RNA-seq data. Here, we present a novel method named FWP (Feature Weight Pro) that helps measure cancerous transcriptome using transcriptomic data. The workflow of FWP is, first, to calculate feature weights using the training dataset, and then, for each sample in the testing dataset, calculate the feature-weight based final score by combining the cohort-wide and sample-specific information. Those two types of information are utilized through conducting weighted principal component analysis and calculating correlation perturbations. The effectiveness and superiority of FWP over other methods are shown by using bulk, single-cell, and spatial RNA-seq data of multiple cancer types. In addition, the high robustness and efficiency of FWP are also demonstrated by using different numbers of features and cells, respectively. FWP is available at https://github.com/jumphone/fwp .

Integrative multi-omic sequencing reveals the MMTV-Myc mouse model mimics human breast cancer heterogeneity

BackgroundBreast cancer is a complex and heterogeneous disease with distinct subtypes and molecular profiles corresponding to different clinical outcomes. Mouse models of breast cancer are widely used, but their relevance in capturing the heterogeneity of human disease is unclear. Previous studies have shown the heterogeneity at the gene expression level for the MMTV-Myc model, but have only speculated on the underlying genetics.MethodsTumors from the microacinar, squamous, and EMT histological subtypes of the MMTV-Myc mouse model of breast cancer underwent whole genome sequencing. The genomic data obtained were then integrated with previously obtained matched sample gene expression data and extended to additional samples of each histological subtype, totaling 42 gene expression samples. High correlation was observed between genetic copy number events and resulting gene expression by both Spearman’s rank correlation coefficient and the Kendall rank correlation coefficient. These same genetic events are conserved in humans and are indicative of poor overall survival by Kaplan–Meier analysis. A supervised machine learning algorithm trained on METABRIC gene expression data was used to predict the analogous human breast cancer intrinsic subtype from mouse gene expression data.ResultsHerein, we examine three common histological subtypes of the MMTV-Myc model through whole genome sequencing and have integrated these results with gene expression data. Significantly, key genomic alterations driving cell signaling pathways were well conserved within histological subtypes. Genomic changes included frequent, co-occurring mutations in KIT and RARA in the microacinar histological subtype as well as SCRIB mutations in the EMT subtype. EMT tumors additionally displayed strong KRAS activation signatures downstream of genetic activating events primarily ascribed to KRAS activating mutations, but also FGFR2 amplification. Analogous genetic events in human breast cancer showed stark decreases in overall survival. In further analyzing transcriptional heterogeneity of the MMTV-Myc model, we report a supervised machine learning model that classifies MMTV-Myc histological subtypes and other mouse models as being representative of different human intrinsic breast cancer subtypes.ConclusionsWe conclude the well-established MMTV-Myc mouse model presents further opportunities for investigation of human breast cancer heterogeneity.

THU500 Geospatial Analysis To Quantify Spatial Heterogeneity Of Tumor Microenvironment

Abstract Disclosure: J. Yoo: None. C. Mitra: None. Y. Yue: None. A. Soliman: None. Z. Madak Erdogan: None. Metabolic heterogeneity, a concept describing distinct differences in nutrient metabolism within the cells of a healthy or diseased tissue, is a common challenge in development and cancer research. Current methods, such as histology and Magnetic Resonance Imaging (MRI), cannot visualize nutrient uptake into single cells, making it difficult to understand the heterogeneity that affects cellular growth and metabolism. Visium Spatial Transcriptomics, a method that shows regional gene expression, is emerging as a powerful spatial analysis tool, and we aim to integrate this transcriptomic method with other biologically relevant datasets to highlight the importance of cell-microenvironment interactions. We propose to leverage geostatistical tools to model the multivariate covariance structure of genes expression profiles to allow: 1) understanding of their spatial characteristics and covariance changes under certain conditions (e.g. drug treatment or damage), 2) using these covariance structures to simulate synthetic samples at a scale sufficient to train the state of art neural network architectures, and 3) incorporating spatial awareness directly in the machine learning/deep learning models by enriching the spatial transcriptomics data using distance proximity functions. We will quantify the tumor microenvironment spatial heterogeneity using different approaches. First, we will analyze the degree of spatial auto-correlation to estimate the ranges of spatial heterogeneity within a tumor. We also plan to quantify the degree of anisotropy to identify if the tumor heterogeneity is happening along specific axis. Different local clustering indicators will be also calculated, such as the Getis-Ord (Gi*) statistic and Local Moran's I to identify the prevalence of hot and cold spots in the tumor. These hot and cold spots will be used to segment the tumor area into sub-regions and further quantify the local heterogeneity with a single tumor. We hypothesize that transcriptional heterogeneity reflects phenotypic differences in different regions of healthy or diseased tissue, rendering cells in certain regions to display a differential response to external stimuli (e.g. diet or drugs). Integration of multiple information (e.g. retrospective patient data) with different spatial heterogeneity indicators identified from the geospatial analyses could aid in identification of patients who are resistant to cancer therapies and further advance personalized therapy. Presentation: Thursday, June 15, 2023

Single-cell RNA sequencing analysis reveals transcriptional heterogeneity of multiple primary lung cancer.

With the advancements in early diagnosis, more and more patients with multiple primary lung cancer (MPLC) have been identified. However, the progression of MPLC involves complex changes in cell composition and metabolic function, which remains largely controversial. Our study aims to comprehensively reveal the cellular characteristics and inter-cellular connections of MPLC. We performed scRNA-seq from 23 samples of six MPLC patients, combined with bulk whole-exome sequencing. We performed trajectory analysis to investigate the transition of different cell types during the development of MPLC. A total of 167397 cells were sequenced derived from tumour and adjacent tissues of MPLC patients, and tumour, normal, immune and stromal cells were identified. Two states of epithelial cells were identified, which were associated with immune response and cell death, respectively. Furthermore, both CD8+ naïve and memory T cells participated in the differentiation of CD8+ T cells. The terminal states of CD8+ T cells were exhausted T cells and cytotoxic T cells, which positively regulated cell death and were implicated in the regulation of cytokine production, respectively. Two main subpopulations of B cells with distinct functions were identified, which participate in the regulation of the immune response and antigen presentation, respectively. In addition, we found a specific type of endothelial cells that were abundant in tumour samples, with an increasing trend from normal to tumour samples. Our study showed the comprehensive landscape of different cells of MPLC, which might reveal the key cellular mechanisms and, therefore, may provide new insights into the early diagnosis and treatment of MPLC.

Divergent molecular events underlying initial T-cell commitment in human prenatal and postnatal thymus.

Intrathymic T-cell development is a coordinated process accompanied by dynamic changes in gene expression. Although the transcriptome characteristics of developing T cells in both human fetal and postnatal thymus at single-cell resolution have been revealed recently, the differences between human prenatal and postnatal thymocytes regarding the ontogeny and early events of T-cell development still remain obscure. Moreover, the transcriptional heterogeneity and posttranscriptional gene expression regulation such as alternative polyadenylation at different stages are also unknown. In this study, we performed integrative single-cell analyses of thymocytes at distinct developmental stages. The subsets of prenatal CD4-CD8- double-negative (DN) cells, the most immature thymocytes responsible for T-cell lineage commitment, were characterized. By comprehensively comparing prenatal and postnatal DN cells, we revealed significant differences in some key gene expressions. Specifically, prenatal DN subpopulations exhibited distinct biological processes and markedly activated several metabolic programs that may be coordinated to meet the required bioenergetic demands. Although showing similar gene expression patterns along the developmental path, prenatal and postnatal thymocytes were remarkably varied regarding the expression dynamics of some pivotal genes for cell cycle, metabolism, signaling pathway, thymus homing, and T-cell commitment. Finally, we quantified the transcriptome-wide changes in alternative polyadenylation across T-cell development and found diverse preferences of polyadenylation site usage in divergent populations along the T-cell commitment trajectory. In summary, our results revealed transcriptional heterogeneity and a dynamic landscape of alternative polyadenylation during T-cell development in both human prenatal and postnatal thymus, providing a comprehensive resource for understanding T lymphopoiesis in human thymus.

White Adipose Tissue Heterogeneity in the Single-Cell Era: From Mice and Humans to Cattle.

Adipose tissue is a major modulator of metabolic function by regulating energy storage and by acting as an endocrine organ through the secretion of adipokines. With the advantage of next-generation sequencing-based single-cell technologies, adipose tissue has been studied at single-cell resolution, thus providing unbiased insight into its molecular composition. Recent single-cell RNA sequencing studies in human and mouse models have dissected the transcriptional cellular heterogeneity of subcutaneous (SAT), visceral (VAT), and intramuscular (IMAT) white adipose tissue depots and revealed unique populations of adipose tissue progenitor cells, mature adipocytes, immune cell, vascular cells, and mesothelial cells that play direct roles on adipose tissue function and the development of metabolic disorders. In livestock species, especially in bovine, significant gaps of knowledge remain in elucidating the roles of adipose tissue cell types and depots on driving the pathogenesis of metabolic disorders and the distinct fat deposition in VAT, SAT, and IMAT in meat animals. This review summarizes the current knowledge on the transcriptional and functional cellular diversity of white adipose tissue revealed by single-cell approaches and highlights the depot-specific function of adipose tissue in different mammalian species, with a particular focus on recent findings and future implications in cattle.

Transcriptional Heterogeneity Overcomes Super-Enhancer Disrupting Drug Combinations in Multiple Myeloma.

These results highlight the dependence of MM on IKZF1-bound super-enhancers, which can be effectively targeted by a potent therapeutic combination pairing IMiD-mediated degradation of IKZF1 and IKZF3 with EP300 inhibition. They also identify AP-1 factors as an unrecognized mechanism of IMiD resistance in MM. See related article by Neri, Barwick, et al., p. 56. See related commentary by Yun and Cleveland, p. 5. This article is featured in Selected Articles from This Issue, p. 4.

Single-cell transcriptome analysis dissects lncRNA-associated gene networks in Arabidopsis

The plant genome produces an extremely large collection of long noncoding RNAs (lncRNAs) that are generally expressed in a context-specific manner and have pivotal roles in regulation of diverse biological processes. Here, we mapped the transcriptional heterogeneity of lncRNAs and their associated gene regulatory networks at single-cell resolution. We generated a comprehensive cell atlas at the whole-organism level by integrative analysis of 28 published single-cell RNA sequencing (scRNA-seq) datasets from juvenile Arabidopsis seedlings. We then provided an in-depth analysis of cell-type-related lncRNA signatures that show expression patterns consistent with canonical protein-coding gene markers. We further demonstrated that the cell-type-specific expression of lncRNAs largely explains their tissue specificity. In addition, we predicted gene regulatory networks on the basis of motif enrichment and co-expression analysis of lncRNAs and mRNAs, and we identified putative transcription factors orchestrating cell-type-specific expression of lncRNAs. The analysis results are available at the single-cell-based plant lncRNA atlas database (scPLAD; https://biobigdata.nju.edu.cn/scPLAD/). Overall, this work demonstrates the power of integrative single-cell data analysis applied to plant lncRNA biology and provides fundamental insights into lncRNA expression specificity and associated gene regulation.

P-177 Transcriptional heterogeneity overcomes super-enhancer disrupting drug combinations in multiple myeloma (MM)

P-177 Transcriptional heterogeneity overcomes super-enhancer disrupting drug combinations in multiple myeloma (MM)

Dynamic single-cell regulomes characterize human peripheral blood innate lymphoid cell subpopulations

Innate lymphoid cells (ILCs) are plastic immune cells divided into 3 main subsets, characterized by distinct phenotypic and functional profiles. Using single cell approaches, heightened heterogeneity of mouse ILCs has been appreciated, imprinted by tissue signals that shape their transcriptome and epigenome. Intra-subset diversity has also been observed in human ILCs. However, combined transcriptomic and epigenetic analyses of single ILCs in humans are lacking. Here, we show high transcriptional and epigenetic heterogeneity among human circulating ILCs in healthy individuals. We describe phenotypically distinct subclusters and diverse chromatin accessibility within main ILC populations, compatible with differentially poised states. We validate the use of this healthy donor-based analysis as resource dataset to help inferring ILC changes occurring in disease conditions. Overall, our work provides insights in the complex human ILC biology. We anticipate it to facilitate hypothesis-driven studies in patients, without the need to perform single cell OMICs using precious patients' material.

Deciphering the landscape of transcriptional heterogeneity across cancer

By integrating scRNA-seq datasets across 77 studies and 24 cancer types, in Nature, Gavish etal. uncover recurrent patterns of gene expression that explain a significant proportion of transcriptomic heterogeneity observed in cancer and explore their functional significance.