Heterogeneous Gene Expression Research Articles

Three-dimensional molecular cartography of human cerebral organoids revealed by double-barcoded spatial transcriptomics

Spatially resolved transcriptomics is revolutionizing our understanding of complex tissues, but scaling these approaches to multiple tissue sections and three-dimensional tissue reconstruction remains challenging and cost prohibitive. In this work, we present a low-cost strategy for manufacturing molecularly double-barcoded DNA arrays, enabling large-scale spatially resolved transcriptomics studies. We applied this technique to spatially resolve gene expression in several human brain organoids, including the reconstruction of a three-dimensional view from multiple consecutive sections, revealing gene expression heterogeneity throughout the tissue.

The spread of interferon-γ in melanomas is highly spatially confined, driving nongenetic variability in tumor cells

Interferon-γ (IFNγ) is a critical antitumor cytokine that has varied effects on different cell types. The global effect of IFNγ in the tumor depends on which cells it acts upon and the spatial extent of its spread. Reported measurements of IFNγ spread vary dramatically in different contexts, ranging from nearest-neighbor signaling to perfusion throughout the entire tumor. Here, we apply theoretical considerations to experiments both in vitro and in vivo to study the spread of IFNγ in melanomas. We observe spatially confined niches of IFNγ signaling in 3-D mouse melanoma cultures and human tumors that generate cellular heterogeneity in gene expression and alter the susceptibility of affected cells to T cell killing. Widespread IFNγ signaling only occurs when niches overlap due to high local densities of IFNγ-producing T cells. We measured length scales of ~30 to 40 μm for IFNγ spread in B16 mouse melanoma cultures and human primary cutaneous melanoma. Our results are consistent with IFNγ spread being governed by a simple diffusion-consumption model and offer insight into how the spatial organization of T cells contributes to intratumor heterogeneity in inflammatory signaling, gene expression, and immune-mediated clearance. Solid tumors are often viewed as collections of diverse cellular "neighborhoods": Our work provides a general explanation for such nongenetic cellular variability due to confinement in the spread of immune mediators.

An information-theoretic approach to single cell sequencing analysis

BackgroundSingle-cell sequencing (sc-Seq) experiments are producing increasingly large data sets. However, large data sets do not necessarily contain large amounts of information.ResultsHere, we formally quantify the information obtained from a sc-Seq experiment and show that it corresponds to an intuitive notion of gene expression heterogeneity. We demonstrate a natural relation between our notion of heterogeneity and that of cell type, decomposing heterogeneity into that component attributable to differential expression between cell types (inter-cluster heterogeneity) and that remaining (intra-cluster heterogeneity). We test our definition of heterogeneity as the objective function of a clustering algorithm, and show that it is a useful descriptor for gene expression patterns associated with different cell types.ConclusionsThus, our definition of gene heterogeneity leads to a biologically meaningful notion of cell type, as groups of cells that are statistically equivalent with respect to their patterns of gene expression. Our measure of heterogeneity, and its decomposition into inter- and intra-cluster, is non-parametric, intrinsic, unbiased, and requires no additional assumptions about expression patterns. Based on this theory, we develop an efficient method for the automatic unsupervised clustering of cells from sc-Seq data, and provide an R package implementation.

Heterogeneity in leukemia cells that escape drug-induced senescence-like state

Erythropoietin (EPO) suppresses drug-induced apoptosis in EPO-receptor-positive leukemia cells and allows cells to persist after drug treatment by promoting cellular senescence. Importantly a small proportion of senescent cells can re-enter the cell cycle and resume proliferation after drug treatment, resulting in disease recurrence/persistence. Using a single-cell assay to track individual cells that exit a drug-induced senescence-like state, we show that cells exhibit asynchronous exit from a senescent-like state, and display different rates of proliferation. Escaped cells retain sensitivity to drug treatment, but display inter-clonal variability. We also find heterogeneity in gene expression with some of the escaped clones retaining senescence-associated gene expression. Senescent leukemia cells exhibit changes in gene expression that affect metabolism and senescence-associated secretory phenotype (SASP)-related genes. Herein, we generate a senescence gene signature and show that this signature is a prognostic marker of worse overall survival in AML and multiple other cancers. A portion of senescent leukemia cells depend on lysosome activity; chloroquine, an inhibitor of lysosome activity, promotes senolysis of some senescent leukemia cells. Our study indicates that the serious risks associated with the use of erythropoietin-stimulating agents (ESAs) in anemic cancer patients may be attributed to their ability to promote drug-tolerant cancer cells through the senescence program.

Heterogeneity in quiescent Müller glia in the uninjured zebrafish retina drive differential responses following photoreceptor ablation.

Loss of neurons in the neural retina is a leading cause of vision loss. While humans do not possess the capacity for retinal regeneration, zebrafish can achieve this through activation of resident Müller glia. Remarkably, despite the presence of Müller glia in humans and other mammalian vertebrates, these cells lack an intrinsic ability to contribute to regeneration. Upon activation, zebrafish Müller glia can adopt a stem cell-like state, undergo proliferation and generate new neurons. However, the underlying molecular mechanisms of this activation subsequent retinal regeneration remains unclear. To address this, we performed single-cell RNA sequencing (scRNA-seq) and report remarkable heterogeneity in gene expression within quiescent Müller glia across distinct dorsal, central and ventral retina pools of such cells. Next, we utilized a genetically driven, chemically inducible nitroreductase approach to study Müller glia activation following selective ablation of three distinct photoreceptor subtypes: long wavelength sensitive cones, short wavelength sensitive cones, and rods. There, our data revealed that a region-specific bias in activation of Müller glia exists in the zebrafish retina, and this is independent of the distribution of the ablated cell type across retinal regions. Notably, gene ontology analysis revealed that injury-responsive dorsal and central Müller glia express genes related to dorsal/ventral pattern formation, growth factor activity, and regulation of developmental process. Through scRNA-seq analysis, we identify a shared genetic program underlying initial Müller glia activation and cell cycle entry, followed by differences that drive the fate of regenerating neurons. We observed an initial expression of AP-1 and injury-responsive transcription factors, followed by genes involved in Notch signaling, ribosome biogenesis and gliogenesis, and finally expression of cell cycle, chromatin remodeling and microtubule-associated genes. Taken together, our findings document the regional specificity of gene expression within quiescent Müller glia and demonstrate unique Müller glia activation and regeneration features following neural ablation. These findings will improve our understanding of the molecular pathways relevant to neural regeneration in the retina.

Immune cell identifier and classifier (ImmunIC) for single cell transcriptomic readouts

Single cell RNA sequencing has a central role in immune profiling, identifying specific immune cells as disease markers and suggesting therapeutic target genes of immune cells. Immune cell-type annotation from single cell transcriptomics is in high demand for dissecting complex immune signatures from multicellular blood and organ samples. However, accurate cell type assignment from single-cell RNA sequencing data alone is complicated by a high level of gene expression heterogeneity. Many computational methods have been developed to respond to this challenge, but immune cell annotation accuracy is not highly desirable. We present ImmunIC, a simple and robust tool for immune cell identification and classification by combining marker genes with a machine learning method. With over two million immune cells and half-million non-immune cells from 66 single cell RNA sequencing studies, ImmunIC shows 98% accuracy in the identification of immune cells. ImmunIC outperforms existing immune cell classifiers, categorizing into ten immune cell types with 92% accuracy. We determine peripheral blood mononuclear cell compositions of severe COVID-19 cases and healthy controls using previously published single cell transcriptomic data, permitting the identification of immune cell-type specific differential pathways. Our publicly available tool can maximize the utility of single cell RNA profiling by functioning as a stand-alone bioinformatic cell sorter, advancing cell-type specific immune profiling for the discovery of disease-specific immune signatures and therapeutic targets.

Single-cell transcriptional uncertainty landscape of cell differentiation.

Background: Single-cell studies have demonstrated the presence of significant cell-to-cell heterogeneity in gene expression. Whether such heterogeneity is only a bystander or has a functional role in the cell differentiation process is still hotly debated. Methods: In this study, we quantified and followed single-cell transcriptional uncertainty - a measure of gene transcriptional stochasticity in single cells - in 10 cell differentiation systems of varying cell lineage progressions, from single to multi-branching trajectories, using the stochastic two-state gene transcription model. Results: By visualizing the transcriptional uncertainty as a landscape over a two-dimensional representation of the single-cell gene expression data, we observed universal features in the cell differentiation trajectories that include: (i) a peak in single-cell uncertainty during transition states, and in systems with bifurcating differentiation trajectories, each branching point represents a state of high transcriptional uncertainty; (ii) a positive correlation of transcriptional uncertainty with transcriptional burst size and frequency; (iii) an increase in RNA velocity preceding the increase in the cell transcriptional uncertainty. Conclusions: Our findings suggest a possible universal mechanism during the cell differentiation process, in which stem cells engage stochastic exploratory dynamics of gene expression at the start of the cell differentiation by increasing gene transcriptional bursts, and disengage such dynamics once cells have decided on a particular terminal cell identity. Notably, the peak of single-cell transcriptional uncertainty signifies the decision-making point in the cell differentiation process.

Single-cell sequencing analysis of peripheral blood in patients with moyamoya disease

BackgroundAt present, the etiology of moyamoya disease is not clear, and it is necessary to explore the mechanism of its occurrence and development. Although some bulk sequencing data have previously revealed transcriptomic changes in Moyamoya disease, single-cell sequencing data has been lacking.MethodsTwo DSA(Digital Subtraction Angiography)-diagnosed patients with moyamoya disease were recruited between January 2021 and December 2021. Their peripheral blood samples were single-cell sequenced. CellRanger(10 x Genomics, version 3.0.1) was used to process the raw data, demultiplex cellular barcodes, map reads to the transcriptome, and dowm-sample reads(as required to generate normalized aggregate data across samples). There were 4 normal control samples, including two normal samples GSM5160432 and GSM5160434 of GSE168732, and two normal samples of GSE155698, namely GSM4710726 and GSM4710727. Weighted co-expression network analysis was used to explore the gene sets associated with moyamoya disease. GO analysis and KEGG analysis were used to explore gene enrichment pathways. Pseudo-time series analysis and cell interaction analysis were used to explore cell differentiation and cell interaction.ResultsFor the first time, we present a peripheral blood single cell sequencing landscape of Moyamoya disease, revealing cellular heterogeneity and gene expression heterogeneity. In addition, by combining with WGCNA analysis in public database and taking intersection, the key genes in moyamoya disease were obtained. namely PTP4A1, SPINT2, CSTB, PLA2G16, GPX1, HN1, LGALS3BP, IFI6, NDRG1, GOLGA2, LGALS3. Moreover, pseudo-time series analysis and cell interaction analysis revealed the differentiation of immune cells and the relationship between immune cells in Moyamoya disease.ConclusionsOur study can provide information for the diagnosis and treatment of moyamoya disease.

Serum Response Factor Reduces Gene Expression Noise and Confers Cell State Stability.

The role of serum response factor (Srf), a central mediator of actin dynamics and mechanical signaling, in cell identity regulation is debated to be either a stabilizer or a destabilizer. We investigated the role of Srf in cell fate stability using mouse pluripotent stem cells. Despite the fact that serum-containing cultures yield heterogeneous gene expression, deletion of Srf in mouse pluripotent stem cells leads to further exacerbated cell state heterogeneity. The exaggerated heterogeneity is detectible not only as increased lineage priming but also as the developmentally earlier 2C-like cell state. Thus, pluripotent cells explore more variety of cellular states in both directions of development surrounding naïve pluripotency, a behavior that is constrained by Srf. These results support that Srf functions as a cell state stabilizer, providing rationale for its functional modulation in cell fate intervention and engineering.

Generalized Cell Type Annotation and Discovery for Single-Cell RNA-Seq Data

The rapid development of single-cell RNA sequencing (scRNA-seq) technology allows us to study gene expression heterogeneity at the cellular level. Cell annotation is the basis for subsequent downstream analysis in single-cell data mining. Existing methods rarely explore the fine-grained semantic knowledge of novel cell types absent from the reference data and usually susceptible to batch effects on the classification of seen cell types. Taking into consideration these limitations, this paper proposes a new and practical task called generalized cell type annotation and discovery for scRNA-seq data. In this task, cells of seen cell types are given class labels, while cells of novel cell types are given cluster labels instead of a unified “unassigned” label. To address this problem, we carefully design a comprehensive evaluation benchmark and propose a novel end-to-end algorithm framework called scGAD. Specifically, scGAD first builds the intrinsic correspondence across the reference and target data by retrieving the geometrically and semantically mutual nearest neighbors as anchor pairs. Then we introduce an anchor-based self-supervised learning module with a connectivity-aware attention mechanism to facilitate model prediction capability on unlabeled target data. To enhance the inter-type separation and intra-type compactness, we further propose a confidential prototypical self-supervised learning module to uncover the consensus category structure of the reference and target data. Extensive results on massive real datasets demonstrate the superiority of scGAD over various state-of-the-art clustering and annotation methods.

Incidence of biofilms among the multidrug resistant E. coli, isolated from urinary tract infections in the Nilgiris district, South India.

Multidrug resistant uropathogenic Escherichia coli (MDRUPEC) significantly correlates with recurrent, complicated, and persistent urinary tract infection (UTI). The leading cause of multidrug resistance is the ability of E. coli to form biofilms. The physiological heterogeneity, genetic incontinency, and putative events in gene expression of biofilms render them resistant to antimicrobials and the host immune system. Understanding the determinants of antimicrobial resistance and its correlation with biofilm formations will, therefore, help the development of a better strategy for treating biofilm-associated UTIs. The present study reports on the in vitro detection of biofilm formation among multidrug resistant E. coli strains isolated from urine, the major cause of communal, nosocomial, and food-borne uropathogenic UTI. This is a cross-sectional study conducted in and around Ooty, Nilgiris, India. From the 869 urine samples analyzed for UTI, 29.34% were found to be caused by E. coli. Among this about 23.92% were found to be multidrug resistant. Among the multidrug resistant E. coli isolates, 36.06% of them were potent biofilm producers. E. coli biofilms (n = 22) were resistant to the antibiotics used to treat UTI, namely, amikacin, amoxicillin, ampicillin, cefepime, cefoperazone, cefotaxime, ceftriaxone, cefuroxime, cephalotin, ciprofloxacin, co-trimoxazole, gentamicin, levofloxacin, and nalidixic acid, but sensitive to imipenem and meropenem. All the biofilm producers exhibited motility and hemaggultination but none were positive for hemolysin production. The isolated E. coli biofilms were confirmed by VITEK R2 Compact (bioMerieux, France) and 16S rRNA gene sequencing.

Enhancing the sensitivity of bacterial single-cell RNA sequencing using RamDA-seq and Cas9-based rRNA depletion

Bacterial populations exhibit heterogeneity in gene expression, which facilitates their survival and adaptation to unstable and unpredictable environments through the bet-hedging strategy. However, unraveling the rare subpopulations and heterogeneity in gene expression using population-level gene expression analysis remains a challenging task. Single-cell RNA sequencing (scRNA-seq) has the potential to identify rare subpopulations and capture heterogeneity in bacterial populations, but standard methods for scRNA-seq in bacteria are still under development, mainly due to differences in mRNA abundance and structure between eukaryotic and prokaryotic organisms. In this study, we present a hybrid approach that combines random displacement amplification sequencing (RamDA-seq) with Cas9-based rRNA depletion for scRNA-seq in bacteria. This approach allows cDNA amplification and subsequent sequencing library preparation from low-abundance bacterial RNAs. We evaluated its sequenced read proportion, gene detection sensitivity, and gene expression patterns from the dilution series of total RNA or the sorted single Escherichia coli cells. Our results demonstrated the detection of more than 1000 genes, about 24% of the genes in the E.coli genome, from single cells with less sequencing effort compared to conventional methods. We observed gene expression clusters between different cellular proliferation states or heat shock treatment. The approach demonstrated high detection sensitivity in gene expression analysis compared to current bacterial scRNA-seq methods and proved to be an invaluable tool for understanding the ecology of bacterial populations and capturing the heterogeneity of bacterial gene expression.

MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment

Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

Tissue heterogeneity of mitochondrial activity, biogenesis and mitochondrial protein gene expression in buffalo.

Cellular metabolism is most invariant process, occurring in all living organisms, which involves mitochondrial proteins from both nuclear and mitochondrial genomes. The mitochondrial DNA (mtDNA) copy number, protein-coding genes (mtPCGs) expression, and activity vary between various tissues to fulfill specific energy demands across the tissues. In present study, we investigated the OXPHOS complexes and citrate synthase activity in isolated mitochondria from various tissues of freshly slaughtered buffaloes (n = 3). Further, the evaluation of tissue-specific diversity based on the quantification of mtDNA copy numbers was performed and also comprised an expression study of 13 mtPCGs. We found that the functional activity of individual OXPHOS complex I was significantly higher in the liver compared to muscle and brain. Additionally, OXPHOS complex III and V activities was observed significantly higher levels in liver compared to heart, ovary, and brain. Similarly, CS-specific activity differs between tissues, with the ovary, kidney, and liver having significantly greater. Furthermore, we revealed the mtDNA copy number was strictly tissue-specific, with muscle and brain tissues exhibiting the highest levels. Among 13 PCGs expression analyses, mRNA abundances in all genes were differentially expressed among the different tissue. Overall, our results indicate the existence of a tissue-specific variation in mitochondrial activity, bioenergetics, and mtPCGs expression among various types of buffalo tissues. This study serves as a critical first stage in gathering vital comparable data about the physiological function of mitochondria in energy metabolism in distinct tissues, laying the groundwork for future mitochondrial based diagnosis and research.

Phylogenetic comparative analysis of single-cell transcriptomes reveals constrained accumulation of gene expression heterogeneity during clonal expansion.

In the same way that a phylogeny summarizes the evolutionary history of species, a cell lineage tree describes the process of clonal expansion, in which gene expression differences between cells naturally accrue as a result of stochastic partitioning and imperfect expression control. How is functional homeostasis, a key factor in the biological function of any population of cells, maintained in the face of such continuous accumulation of transcriptomic heterogeneity remains largely unresolved. To answer this question, we experimentally determined the single-cell transcriptomes and lineage relationships of up to 50% cells in single-HEK293-seeded colonies. Phylogenetic comparative analyses of the single-cell transcriptomes on the cell lineage tree revealed three lines of evidence for the constrained accumulation of transcriptome heterogeneity among cells, including rapid saturation of transcriptomic heterogeneity upon four cell divisions, reduced expression differences within sub-trees closer to expression boundaries, and co-fluctuations among genes. Our analyses showcased the applicability of phylogenetic comparative methods in cell lineage trees, demonstrated the constrained accumulation of transcriptomic heterogeneity and provided novel insight into the functional homeostasis of cell populations.

Defining the genetic program pre-specifying early cardiac progenitor cells fate

During gastrulation, cardiac progenitors (CPs) are identified by the expression of the transcription factor Mesp1. Previous studies have demonstrated that CPs are already spatiotemporally restricted to specific cardiac lineages by that stage. Thus, the heart is built from distinct CPs that display different potential. Recent transcriptional analyses have uncovered the molecular heterogeneity of CPs. In order to understand how CP heterogeneity affects their fate in the adult heart, our aim is to define and modulate the mouse genetic program or “heart code” driving CP specification. We are focusing, in particular, on homeodomain transcription factors that are heterogeneously expressed in CPs. To this end, our objectives are to identify CP sub-populations by defining the spatiotemporal pattern of homeodomain genes during gastrulation and their final cardiac contributions. The role of the homeodomain genes in regulating the pre-specification of CPs are assessed by modulating their expression and analyzing how it impacts CP subpopulations in vivo and in vitro. Our promising preliminary results have already pinpointed the spatial heterogeneity of homeodomain gene expression during gastrulation and have suggested an important role for Lhx1 in Mesp1 + progenitors to correctly form head structures. Together these results will allow the dynamic characterization of heterogeneity within CPs and the identification of putative factors that could trigger the differentiation toward one particular cardiac cell type. These studies have important implications for better understanding the etiology of congenital cardiac malformations and should be the starting point of further applications for improving cell therapy during cardiac repair.

Heterogeneity of Repolarization and Cell-Cell Variability of Cardiomyocyte Remodeling Within the Myocardial Infarction Border Zone Contribute to Arrhythmia Susceptibility.

After myocardial infarction, the infarct border zone (BZ) is the dominant source of life-threatening arrhythmias, where fibrosis and abnormal repolarization create a substrate for reentry. We examined whether repolarization abnormalities are heterogeneous within the BZ in vivo and could be related to heterogeneous cardiomyocyte remodeling. Myocardial infarction was induced in domestic pigs by 120-minute ischemia followed by reperfusion. After 1 month, remodeling was assessed by magnetic resonance imaging, and electroanatomical mapping was performed to determine the spatial distribution of activation-recovery intervals. Cardiomyocytes were isolated and tissue samples collected from the BZ and remote regions. Optical recording allowed assessment of action potential duration (di-8-ANEPPS, stimulation at 1 Hz, 37 °C) of large cardiomyocyte populations while gene expression in cardiomyocytes was determined by single nuclear RNA sequencing. In vivo, activation-recovery intervals in the BZ tended to be longer than in remote with increased spatial heterogeneity evidenced by a greater local SD (3.5±1.3 ms versus remote: 2.0±0.5 ms, P=0.036, npigs=5). Increased activation-recovery interval heterogeneity correlated with enhanced arrhythmia susceptibility. Cellular population studies (ncells=635-862 cells per region) demonstrated greater heterogeneity of action potential duration in the BZ (SD, 105.9±17.0 ms versus remote: 73.9±8.6 ms; P=0.001; npigs=6), which correlated with heterogeneity of activation-recovery interval in vivo. Cell-cell gene expression heterogeneity in the BZ was evidenced by increased Euclidean distances between nuclei of the BZ (12.1 [9.2-15.0] versus 10.6 [7.5-11.6] in remote; P<0.0001). Differentially expressed genes characterizing BZ cardiomyocyte remodeling included hypertrophy-related and ion channel-related genes with high cell-cell variability of expression. These gene expression changes were driven by stress-responsive TFs (transcription factors). In addition, heterogeneity of left ventricular wall thickness was greater in the BZ than in remote. Heterogeneous cardiomyocyte remodeling in the BZ is driven by uniquely altered gene expression, related to heterogeneity in the local microenvironment, and translates to heterogeneous repolarization and arrhythmia vulnerability in vivo.

Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace

Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.

Transforming RNA-Seq gene expression to track cancer progression in the multi-stage early to advanced-stage cancer development.

Cancer progression can be tracked by gene expression changes that occur throughout early-stage to advanced-stage cancer development. The accumulated genetic changes can be detected when gene expression levels in advanced-stage are less variable but show high variability in early-stage. Normalizing advanced-stage expression samples with early-stage and clustering of the normalized expression samples can reveal cancers with similar or different progression and provide insight into clinical and phenotypic patterns of patient samples within the same cancer. This study aims to investigate cancer progression through RNA-Seq expression profiles across the multi-stage process of cancer development. RNA-sequenced gene expression of Diffuse Large B-cell Lymphoma, Lung cancer, Liver cancer, Cervical cancer, and Testicular cancer were downloaded from the UCSC Xena database. Advanced-stage samples were normalized with early-stage samples to consider heterogeneity differences in the multi-stage cancer progression. WGCNA was used to build a gene network and categorized normalized genes into different modules. A gene set enrichment analysis selected key gene modules related to cancer. The diagnostic capacity of the modules was evaluated after hierarchical clustering. Unnormalized RNA-Seq gene expression failed to segregate advanced-stage samples based on selected cancer cohorts. Normalization with early-stage revealed the true heterogeneous gene expression that accumulates across the multi-stage cancer progression, this resulted in well segregated cancer samples. Cancer-specific pathways were enriched in the normalized WGCNA modules. The normalization method was further able to stratify patient samples based on phenotypic and clinical information. Additionally, the method allowed for patient survival analysis, with the Cox regression model selecting gene MAP4K1 in cervical cancer and Kaplan-Meier confirming that upregulation is favourable. The application of the normalization method further enhanced the accuracy of clustering of cancer samples based on how they progressed. Additionally, genes responsible for cancer progression were discovered.

Single-cell transcriptional uncertainty landscape of cell differentiation

Background: Single-cell studies have demonstrated the presence of significant cell-to-cell heterogeneity in gene expression. Whether such heterogeneity is only a bystander or has a functional role in the cell differentiation process is still hotly debated. Methods: In this study, we quantified and followed single-cell transcriptional uncertainty – a measure of gene transcriptional stochasticity in single cells – in 10 cell differentiation systems of varying cell lineage progressions, from single to multi-branching trajectories, using the stochastic two-state gene transcription model. Results: By visualizing the transcriptional uncertainty as a landscape over a two-dimensional representation of the single-cell gene expression data, we observed universal features in the cell differentiation trajectories that include: (i) a peak in single-cell uncertainty during transition states, and in systems with bifurcating differentiation trajectories, each branching point represents a state of high transcriptional uncertainty; (ii) a positive correlation of transcriptional uncertainty with transcriptional burst size and frequency; (iii) an increase in RNA velocity preceding the increase in the cell transcriptional uncertainty. Conclusions: Our findings suggest a possible universal mechanism during the cell differentiation process, in which stem cells engage stochastic exploratory dynamics of gene expression at the start of the cell differentiation by increasing gene transcriptional bursts, and disengage such dynamics once cells have decided on a particular terminal cell identity. Notably, the peak of single-cell transcriptional uncertainty signifies the decision-making point in the cell differentiation process.