Quantitative assessment of cell population diversity in single-cell landscapes

Metabolic Stress Levels Influence the Ability of Myelin Transcription Factors to Regulate β-Cell Identity and Survival

A hallmark of type 2 diabetes (T2D) is endocrine islet β-cell failure, which can occur via cell dysfunction, loss of identity, and/or death. How each is induced remains largely unknown. We used mouse β-cells deficient for myelin transcription factors (Myt TFs; including Myt1, -2, and -3) to address this question. We previously reported that inactivating all three Myt genes in pancreatic progenitor cells (MytPancΔ) caused β-cell failure and late-onset diabetes in mice. Their lower expression in human β-cells is correlated with β-cell dysfunction, and single nucleotide polymorphisms in MYT2 and MYT3 are associated with a higher risk of T2D. We now show that these Myt TF-deficient postnatal β-cells also dedifferentiate by reactivating several progenitor markers. Intriguingly, mosaic Myt TF inactivation in only a portion of islet β-cells did not result in overt diabetes, but this created a condition where Myt TF-deficient β-cells remained alive while activating several markers of Ppy-expressing islet cells. By transplanting MytPancΔ islets into the anterior eye chambers of immune-compromised mice, we directly show that glycemic and obesity-related conditions influence cell fate, with euglycemia inducing several Ppy+ cell markers and hyperglycemia and insulin resistance inducing additional cell death. These findings suggest that the observed β-cell defects in T2D depend not only on their inherent genetic/epigenetic defects but also on the metabolic load.

ARTICLE HIGHLIGHTS

Identification and multimodal characterization of a specialized epithelial cell type associated with Crohn's disease

Crohn's disease (CD) is a complex chronic inflammatory disorder with both gastrointestinal and extra-intestinal manifestations associated immune dysregulation. Analyzing 202,359 cells from 170 specimens across 83 patients, we identify a distinct epithelial cell type in both terminal ileum and ascending colon (hereon as 'LND') with high expression of LCN2, NOS2, and DUOX2 and genes related to antimicrobial response and immunoregulation. LND cells, confirmed by in-situ RNA and protein imaging, are rare in non-IBD controls but expand in active CD, and actively interact with immune cells and specifically express IBD/CD susceptibility genes, suggesting a possible function in CD immunopathogenesis. Furthermore, we discover early and late LND subpopulations with different origins and developmental potential. A higher ratio of late-to-early LND cells correlates with better response to anti-TNF treatment. Our findings thus suggest a potential pathogenic role for LND cells in both Crohn's ileitis and colitis.

A Specialized Epithelial Cell Type Regulating Mucosal Immunity and Driving Human Crohn's Disease

Crohn's disease (CD) is a complex chronic inflammatory disorder that may affect any part of gastrointestinal tract with extra-intestinal manifestations and associated immune dysregulation. To characterize heterogeneity in CD, we profiled single-cell transcriptomics of 170 samples from 65 CD patients and 18 non-inflammatory bowel disease (IBD) controls in both the terminal ileum (TI) and ascending colon (AC). Analysis of 202,359 cells identified a novel epithelial cell type in both TI and AC, featuring high expression of LCN2, NOS2, and DUOX2, and thus is named LND. LND cells, confirmed by high-resolution in-situ RNA imaging, were rarely found in non-IBD controls, but expanded significantly in active CD. Compared to other epithelial cells, genes defining LND cells were enriched in antimicrobial response and immunoregulation. Moreover, multiplexed protein imaging demonstrated that LND cell abundance was associated with immune infiltration. Cross-talk between LND and immune cells was explored by ligand-receptor interactions and further evidenced by their spatial colocalization. LND cells showed significant enrichment of expression specificity of IBD/CD susceptibility genes, revealing its role in immunopathogenesis of CD. Investigating lineage relationships of epithelial cells detected two LND cell subpopulations with different origins and developmental potential, early and late LND. The ratio of the late to early LND cells was related to anti-TNF response. These findings emphasize the pathogenic role of the specialized LND cell type in both Crohn's ileitis and Crohn's colitis and identify novel biomarkers associated with disease activity and treatment response.

A contamination focused approach for optimizing the single-cell RNA-seq experiment

Droplet-based single-cell RNA-seq (scRNA-seq) data are plagued by ambient contaminations caused by nucleic acid material released by dead and dying cells. This material is mixed into the buffer and is co-encapsulated with cells, leading to a lower signal-to-noise ratio. Although there exist computational methods to remove ambient contaminations post-hoc, the reliability of algorithms in generating high-quality data from low-quality sources remains uncertain. Here, we assess data quality before data filtering by a set of quantitative, contamination-based metrics that assess data quality more effectively than standard metrics. Through a series of controlled experiments, we report improvements that can minimize ambient contamination outside of tissue dissociation, via cell fixation, improved cell loading, microfluidic dilution, and nuclei versus cell preparation; many of these parameters are inaccessible on commercial platforms. We provide end-users with insights on factors that can guide their decision-making regarding optimizations that minimize ambient contamination, and metrics to assess data quality.

Th17.1 cell driven sarcoidosis-like inflammation after anti-BCMA CAR T cells in multiple myeloma

Pseudo-progression and flare-up phenomena constitute a novel diagnostic challenge in the follow-up of patients treated with immune-oncology drugs. We present a case study on pulmonary flare-up after Idecabtagen Vicleucel (Ide-cel), a BCMA targeting CAR T-cell therapy, and used single-cell RNA-seq (scRNA-seq) to identify a Th17.1 driven autoimmune mechanism as the biological underpinning of this phenomenon. By integrating datasets of various lung pathological conditions, we revealed transcriptomic similarities between post CAR T pulmonary lesions and sarcoidosis. Furthermore, we explored a noninvasive PET based diagnostic approach and showed that tracers binding to CXCR4 complement FDG PET imaging in this setting, allowing discrimination between immune-mediated changes and true relapse after CAR T-cell treatment. In conclusion, our study highlights a Th17.1 driven autoimmune phenomenon after CAR T, which may be misinterpreted as disease relapse, and that imaging with multiple PET tracers and scRNA-seq could help in this diagnostic dilemma.

Detecting retinal neural and stromal cell classes and ganglion cell subtypes based on transcriptome data with deep transfer learning

MOTIVATION

To develop and assess the accuracy of deep learning models that identify different retinal cell types, as well as different retinal ganglion cell (RGC) subtypes, based on patterns of single-cell RNA sequencing (scRNA-seq) in multiple datasets.

RESULTS

Deep domain adaptation models were developed and tested using three different datasets. The first dataset included 44 808 single retinal cells from mice (39 cell types) with 24 658 genes, the second dataset included 6225 single RGCs from mice (41 subtypes) with 13 616 genes and the third dataset included 35 699 single RGCs from mice (45 subtypes) with 18 222 genes. We used four loss functions in the learning process to align the source and target distributions, reduce misclassification errors and maximize robustness. Models were evaluated based on classification accuracy and confusion matrix. The accuracy of the model for correctly classifying 39 different retinal cell types in the first dataset was ∼92%. Accuracy in the second and third datasets reached ∼97% and 97% in correctly classifying 40 and 45 different RGCs subtypes, respectively. Across a range of seven different batches in the first dataset, the accuracy of the lead model ranged from 74% to nearly 100%. The lead model provided high accuracy in identifying retinal cell types and RGC subtypes based on scRNA-seq data. The performance was reasonable based on data from different batches as well. The validated model could be readily applied to scRNA-seq data to identify different retinal cell types and subtypes.

AVAILABILITY AND IMPLEMENTATION

The code and datasets are available on https://github.com/DM2LL/Detecting-Retinal-Cell-Classes-and-Ganglion-Cell-Subtypes. We have also added the class labels of all samples to the datasets.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MTG16 regulates colonic epithelial differentiation, colitis, and tumorigenesis by repressing E protein transcription factors

Aberrant epithelial differentiation and regeneration contribute to colon pathologies, including inflammatory bowel disease (IBD) and colitis-associated cancer (CAC). Myeloid translocation gene 16 (MTG16, also known as CBFA2T3) is a transcriptional corepressor expressed in the colonic epithelium. MTG16 deficiency in mice exacerbates colitis and increases tumor burden in CAC, though the underlying mechanisms remain unclear. Here, we identified MTG16 as a central mediator of epithelial differentiation, promoting goblet and restraining enteroendocrine cell development in homeostasis and enabling regeneration following dextran sulfate sodium-induced (DSS-induced) colitis. Transcriptomic analyses implicated increased Ephrussi box-binding transcription factor (E protein) activity in MTG16-deficient colon crypts. Using a mouse model with a point mutation that attenuates MTG16:E protein interactions (Mtg16P209T), we showed that MTG16 exerts control over colonic epithelial differentiation and regeneration by repressing E protein-mediated transcription. Mimicking murine colitis, MTG16 expression was increased in biopsies from patients with active IBD compared with unaffected controls. Finally, uncoupling MTG16:E protein interactions partially phenocopied the enhanced tumorigenicity of Mtg16-/- colon in the azoxymethane/DSS-induced model of CAC, indicating that MTG16 protects from tumorigenesis through additional mechanisms. Collectively, our results demonstrate that MTG16, via its repression of E protein targets, is a key regulator of cell fate decisions during colon homeostasis, colitis, and cancer.

Cancer-Associated Fibroblasts and Squamous Epithelial Cells Constitute a Unique Microenvironment in a Mouse Model of Inflammation-Induced Colon Cancer

The tumor microenvironment plays a key role in the pathogenesis of colorectal tumors and contains various cell types including epithelial, immune, and mesenchymal cells. Characterization of the interactions between these cell types is necessary for revealing the complex nature of tumors. In this study, we used single-cell RNA-seq (scRNA-seq) to compare the tumor microenvironments between a mouse model of sporadic colorectal adenoma (Lrig1CreERT2/+;Apc2lox14/+) and a mouse model of inflammation-driven colorectal cancer induced by azoxymethane and dextran sodium sulfate (AOM/DSS). While both models develop tumors in the distal colon, we found that the two tumor types have distinct microenvironments. AOM/DSS tumors have an increased abundance of two populations of cancer-associated fibroblasts (CAFs) compared with APC tumors, and we revealed their divergent spatial association with tumor cells using multiplex immunofluorescence (MxIF) imaging. We also identified a unique squamous cell population in AOM/DSS tumors, whose origins were distinct from anal squamous epithelial cells. These cells were in higher proportions upon administration of a chemotherapy regimen of 5-Fluorouracil/Irinotecan. We used computational inference algorithms to predict cell-cell communication mediated by ligand-receptor interactions and downstream pathway activation, and identified potential mechanistic connections between CAFs and tumor cells, as well as CAFs and squamous epithelial cells. This study provides important preclinical insight into the microenvironment of two distinct models of colorectal tumors and reveals unique roles for CAFs and squamous epithelial cells in the AOM/DSS model of inflammation-driven cancer.

The Tsallis generalized entropy enhances the interpretation of transcriptomics datasets

Background

Identifying differentially expressed genes between experimental conditions is still the gold-standard approach to interpret transcriptomic profiles. Alternative approaches based on diversity measures have been proposed to complement the interpretation of such datasets but are only used marginally.

Methods

Here, we reinvestigated diversity measures, which are commonly used in ecology, to characterize mice pregnancy microenvironments based on a public transcriptome dataset. Mainly, we evaluated the Tsallis entropy function to explore the potential of a collection of diversity measures for capturing relevant molecular event information.

Results

We demonstrate that the Tsallis entropy function provides additional information compared to the traditional diversity indices, such as the Shannon and Simpson indices. Depending on the relative importance given to the most abundant transcripts based on the Tsallis entropy function parameter, our approach allows appreciating the impact of biological stimulus on the inter-individual variability of groups of samples. Moreover, we propose a strategy for reducing the complexity of transcriptome datasets using a maximation of the beta diversity.

Conclusions

We highlight that a diversity-based analysis is suitable for capturing complex molecular events occurring during physiological events. Therefore, we recommend their use through the Tsallis entropy function to analyze transcriptomics data in addition to differential expression analyses.

Single-cell landscapes of primary glioblastomas and matched explants and cell lines show variable retention of inter- and intratumor heterogeneity

Glioblastomas (GBMs) are aggressive brain tumors characterized by extensive inter- and intratumor heterogeneity. Patient-derived models, such as organoids and explants, have recently emerged as useful models to study such heterogeneity, although the extent to which they can recapitulate GBM genomic features remains unclear. Here, we analyze bulk exome and single-cell genome and transcriptome profiles of 12 IDH wild-type GBMs, including two recurrent tumors, and of patient-derived explants (PDEs) and gliomasphere (GS) lines derived from these tumors. We find that PDEs are genetically similar to, and variably retain gene expression characteristics of, their parent tumors. Notably, PDEs appear to exhibit similar levels of transcriptional heterogeneity compared with their parent tumors, whereas GS lines tend to be enriched for cells in a more uniform transcriptional state. The approaches and datasets introduced here will provide a valuable resource to help guide experiments using GBM-derived models, especially in the context of studying cellular heterogeneity.

Defining the early stages of intestinal colonisation by whipworms

Whipworms are large metazoan parasites that inhabit multi-intracellular epithelial tunnels in the large intestine of their hosts, causing chronic disease in humans and other mammals. How first-stage larvae invade host epithelia and establish infection remains unclear. Here we investigate early infection events using both Trichuris muris infections of mice and murine caecaloids, the first in-vitro system for whipworm infection and organoid model for live helminths. We show that larvae degrade mucus layers to access epithelial cells. In early syncytial tunnels, larvae are completely intracellular, woven through multiple live dividing cells. Using single-cell RNA sequencing of infected mouse caecum, we reveal that progression of infection results in cell damage and an expansion of enterocytes expressing of Isg15, potentially instigating the host immune response to the whipworm and tissue repair. Our results unravel intestinal epithelium invasion by whipworms and reveal specific host-parasite interactions that allow the whipworm to establish its multi-intracellular niche.

Comprehensive evaluation of noise reduction methods for single-cell RNA sequencing data

Normalization and batch correction are critical steps in processing single-cell RNA sequencing (scRNA-seq) data, which remove technical effects and systematic biases to unmask biological signals of interest. Although a number of computational methods have been developed, there is no guidance for choosing appropriate procedures in different scenarios. In this study, we assessed the performance of 28 scRNA-seq noise reduction procedures in 55 scenarios using simulated and real datasets. The scenarios accounted for multiple biological and technical factors that greatly affect the denoising performance, including relative magnitude of batch effects, the extent of cell population imbalance, the complexity of cell group structures, the proportion and the similarity of nonoverlapping cell populations, dropout rates and variable library sizes. We used multiple quantitative metrics and visualization of low-dimensional cell embeddings to evaluate the performance on batch mixing while preserving the original cell group and gene structures. Based on our results, we specified technical or biological factors affecting the performance of each method and recommended proper methods in different scenarios. In addition, we highlighted one challenging scenario where most methods failed and resulted in overcorrection. Our studies not only provided a comprehensive guideline for selecting suitable noise reduction procedures but also pointed out unsolved issues in the field, especially the urgent need of developing metrics for assessing batch correction on imperceptible cell-type mixing.

Differential pre-malignant programs and microenvironment chart distinct paths to malignancy in human colorectal polyps

Colorectal cancers (CRCs) arise from precursor polyps whose cellular origins, molecular heterogeneity, and immunogenic potential may reveal diagnostic and therapeutic insights when analyzed at high resolution. We present a single-cell transcriptomic and imaging atlas of the two most common human colorectal polyps, conventional adenomas and serrated polyps, and their resulting CRC counterparts. Integrative analysis of 128 datasets from 62 participants reveals adenomas arise from WNT-driven expansion of stem cells, while serrated polyps derive from differentiated cells through gastric metaplasia. Metaplasia-associated damage is coupled to a cytotoxic immune microenvironment preceding hypermutation, driven partly by antigen-presentation differences associated with tumor cell-differentiation status. Microsatellite unstable CRCs contain distinct non-metaplastic regions where tumor cells acquire stem cell properties and cytotoxic immune cells are depleted. Our multi-omic atlas provides insights into malignant progression of colorectal polyps and their microenvironment, serving as a framework for precision surveillance and prevention of CRC.

Automated quality control and cell identification of droplet-based single-cell data using dropkick

A major challenge for droplet-based single-cell sequencing technologies is distinguishing true cells from uninformative barcodes in data sets with disparate library sizes confounded by high technical noise (i.e., batch-specific ambient RNA). We present dropkick, a fully automated software tool for quality control and filtering of single-cell RNA sequencing (scRNA-seq) data with a focus on excluding ambient barcodes and recovering real cells bordering the quality threshold. By automatically determining data set-specific training labels based on predictive global heuristics, dropkick learns a gene-based representation of real cells and ambient noise, calculating a cell probability score for each barcode. Using simulated and real-world scRNA-seq data, we benchmarked dropkick against conventional thresholding approaches and EmptyDrops, a popular computational method, showing greater recovery of rare cell types and exclusion of empty droplets and noisy, uninformative barcodes. We show for both low- and high-background data sets that dropkick's weakly supervised model reliably learns which genes are enriched in ambient barcodes and draws a multidimensional boundary that is more robust to data set-specific variation than existing filtering approaches. dropkick provides a fast, automated tool for reproducible cell identification from scRNA-seq data that is critical to downstream analysis and compatible with popular single-cell Python packages.

Tensor-Decomposition-Based Unsupervised Feature Extraction in Single-Cell Multiomics Data Analysis

Analysis of single-cell multiomics datasets is a novel topic and is considerably challenging because such datasets contain a large number of features with numerous missing values. In this study, we implemented a recently proposed tensor-decomposition (TD)-based unsupervised feature extraction (FE) technique to address this difficult problem. The technique can successfully integrate single-cell multiomics data composed of gene expression, DNA methylation, and accessibility. Although the last two have large dimensions, as many as ten million, containing only a few percentage of nonzero values, TD-based unsupervised FE can integrate three omics datasets without filling in missing values. Together with UMAP, which is used frequently when embedding single-cell measurements into two-dimensional space, TD-based unsupervised FE can produce two-dimensional embedding coincident with classification when integrating single-cell omics datasets. Genes selected based on TD-based unsupervised FE are also significantly related to reasonable biological roles.

Optimal Estimation of Wasserstein Distance on A Tree with An Application to Microbiome Studies

The weighted UniFrac distance, a plug-in estimator of the Wasserstein distance of read counts on a tree, has been widely used to measure the microbial community difference in microbiome studies. Our investigation however shows that such a plug-in estimator, although intuitive and commonly used in practice, suffers from potential bias. Motivated by this finding, we study the problem of optimal estimation of the Wasserstein distance between two distributions on a tree from the sampled data in the high-dimensional setting. The minimax rate of convergence is established. To overcome the bias problem, we introduce a new estimator, referred to as the moment-screening estimator on a tree (MET), by using implicit best polynomial approximation that incorporates the tree structure. The new estimator is computationally efficient and is shown to be minimax rate-optimal. Numerical studies using both simulated and real biological datasets demonstrate the practical merits of MET, including reduced biases and statistically more significant differences in microbiome between the inactive Crohn's disease patients and the normal controls.

Clinically adaptable polymer enables simultaneous spatial analysis of colonic tissues and biofilms

Microbial influences on host cells depend upon the identities of the microbes, their spatial localization, and the responses they invoke on specific host cell populations. Multimodal analyses of both microbes and host cells in a spatially resolved fashion would enable studies into these complex interactions in native tissue environments, potentially in clinical specimens. While techniques to preserve each of the microbial and host cell compartments have been used to examine tissues and microbes separately, we endeavored to develop approaches to simultaneously analyze both compartments. Herein, we established an original method for mucus preservation using Poloxamer 407 (also known as Pluronic F-127), a thermoreversible polymer with mucus-adhesive characteristics. We demonstrate that this approach can preserve spatially-defined compartments of the mucus bi-layer in the colon and the bacterial communities within, compared with their marked absence when tissues were processed with traditional formalin-fixed paraffin-embedded (FFPE) pipelines. Additionally, antigens for antibody staining of host cells were preserved and signal intensity for 16S rRNA fluorescence in situ hybridization (FISH) was enhanced in poloxamer-fixed samples. This in turn enabled us to integrate multimodal analysis using a modified multiplex immunofluorescence (MxIF) protocol. Importantly, we have formulated Poloxamer 407 to polymerize and cross-link at room temperature for use in clinical workflows. These results suggest that the fixative formulation of Poloxamer 407 can be integrated into biospecimen collection pipelines for simultaneous analysis of microbes and host cells.

An interspecies translation model implicates integrin signaling in infliximab-resistant inflammatory bowel disease

Anti-tumor necrosis factor (anti-TNF) therapy resistance is a major clinical challenge in inflammatory bowel disease (IBD), due, in part, to insufficient understanding of disease-site, protein-level mechanisms. Although proteomics data from IBD mouse models exist, data and phenotype discrepancies contribute to confounding translation from preclinical animal models of disease to clinical cohorts. We developed an approach called translatable components regression (TransComp-R) to overcome interspecies and trans-omic discrepancies between mouse models and human subjects. TransComp-R combines mouse proteomic data with patient pretreatment transcriptomic data to identify molecular features discernable in the mouse data that are predictive of patient response to therapy. Interrogating the TransComp-R models revealed activated integrin pathway signaling in patients with anti-TNF-resistant colonic Crohn's disease (cCD) and ulcerative colitis (UC). As a step toward validation, we performed single-cell RNA sequencing (scRNA-seq) on biopsies from a patient with cCD and analyzed publicly available immune cell proteomics data to characterize the immune and intestinal cell types contributing to anti-TNF resistance. We found that ITGA1 was expressed in T cells and that interactions between these cells and intestinal cell types were associated with resistance to anti-TNF therapy. We experimentally showed that the α₁ integrin subunit mediated the effectiveness of anti-TNF therapy in human immune cells. Thus, TransComp-R identified an integrin signaling mechanism with potential therapeutic implications for overcoming anti-TNF therapy resistance. We suggest that TransComp-R is a generalizable framework for addressing species, molecular, and phenotypic discrepancies between model systems and patients to translationally deliver relevant biological insights.

Use of Single-Cell -Omic Technologies to Study the Gastrointestinal Tract and Diseases, From Single Cell Identities to Patient Features

Single cells are the building blocks of tissue systems that determine organ phenotypes, behaviors, and functions. Understanding the differences between cell types and their activities might provide us with insights into normal tissue physiology, development of disease, and new therapeutic strategies. Although -omic level single-cell technologies are a relatively recent development that have been used only in research settings, these approaches might eventually be used in the clinic. We review the prospects of applying single-cell genome, transcriptome, epigenome, proteome, and metabolome analyses to gastroenterology and hepatology research. Combining data from multi-omic platforms coupled to rapid technological development could lead to new diagnostic, prognostic, and therapeutic approaches.

Dual indexed library design enables compatibility of in-Drop single-cell RNA-sequencing with exAMP chemistry sequencing platforms

Background

The increasing demand of single-cell RNA-sequencing (scRNA-seq) experiments, such as the number of experiments and cells queried per experiment, necessitates higher sequencing depth coupled to high data quality. New high-throughput sequencers, such as the Illumina NovaSeq 6000, enables this demand to be filled in a cost-effective manner. However, current scRNA-seq library designs present compatibility challenges with newer sequencing technologies, such as index-hopping, and their ability to generate high quality data has yet to be systematically evaluated.

Results

Here, we engineered a dual-indexed library structure, called TruDrop, on top of the inDrop scRNA-seq platform to solve these compatibility challenges, such that TruDrop libraries and standard Illumina libraries can be sequenced alongside each other on the NovaSeq. On scRNA-seq libraries, we implemented a previously-documented countermeasure to the well-described problem of index-hopping, demonstrated significant improvements in base-calling accuracy on the NovaSeq, and provided an example of multiplexing twenty-four scRNA-seq libraries simultaneously. We showed favorable comparisons in transcriptional diversity of TruDrop compared with prior inDrop libraries.

Conclusions

Our approach enables cost-effective, high throughput generation of sequencing data with high quality, which should enable more routine use of scRNA-seq technologies.

Poikilosis - pervasive biological variation

Biological systems are dynamic and display heterogeneity at all levels. Ubiquitous heterogeneity, here called for poikilosis, is an integral and important property of organisms and in molecules, systems and processes within them. Traditionally, heterogeneity in biology and experiments has been considered as unwanted noise, here poikilosis is shown to be the normal state. Acceptable variation ranges are called as lagom. Non-lagom, variations that are too extensive, have negative effects, which influence interconnected levels and once the variation is large enough cause a disease and can lead even to death. Poikilosis has numerous applications and consequences e.g. for how to design, analyze and report experiments, how to develop and apply prediction and modelling methods, and in diagnosis and treatment of diseases. Poikilosis-aware new and practical definitions are provided for life, death, senescence, disease, and lagom. Poikilosis is the first new unifying theory in biology since evolution and should be considered in every scientific study.

Poikilosis - pervasive biological variation

Biological systems are dynamic and display heterogeneity at all levels. Ubiquitous heterogeneity, here called for poikilosis, is an integral and important property of organisms and in molecules, systems and processes within them. Traditionally, heterogeneity in biology and experiments has been considered as unwanted noise, here poikilosis is shown to be the normal state. Acceptable variation ranges are called as lagom. Non-lagom, variations that are too extensive, have negative effects, which influence interconnected levels and once the variation is large enough cause a disease and can lead even to death. Poikilosis has numerous applications and consequences e.g. for how to design, analyze and report experiments, how to develop and apply prediction and modelling methods, and in diagnosis and treatment of diseases. Poikilosis-aware new and practical definitions are provided for life, death, senescence, disease, and lagom. Poikilosis is the first new unifying theory in biology since evolution and should be considered in every scientific study.

Application of information theoretical approaches to assess diversity and similarity in single-cell transcriptomics

Single-cell transcriptomics offers a powerful way to reveal the heterogeneity of individual cells. To date, many information theoretical approaches have been proposed to assess diversity and similarity, and characterize the latent heterogeneity in transcriptome data. Diversity implies gene expression variations and can facilitate the identification of signature genes; while, similarity unravels co-expression patterns for cell type clustering. In this review, we summarized 16 measures of information theory used for evaluating diversity and similarity in single-cell transcriptomic data, provide references and shed light on selected theoretical properties when there is a need to select proper measurements in general cases. We further provide an R package assembling discussed approaches to improve the researchers own single-cell transcriptome study. At last, we prospected further applications of diversity and similarity measures in support of depicting heterogeneity in single-cell multi-omics data.

Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines

Naïve CD4⁺ T cells coordinate the immune response by acquiring an effector phenotype in response to cytokines. However, the cytokine responses in memory T cells remain largely understudied. Here we use quantitative proteomics, bulk RNA-seq, and single-cell RNA-seq of over 40,000 human naïve and memory CD4⁺ T cells to show that responses to cytokines differ substantially between these cell types. Memory T cells are unable to differentiate into the Th2 phenotype, and acquire a Th17-like phenotype in response to iTreg polarization. Single-cell analyses show that T cells constitute a transcriptional continuum that progresses from naïve to central and effector memory T cells, forming an effectorness gradient accompanied by an increase in the expression of chemokines and cytokines. Finally, we show that T cell activation and cytokine responses are influenced by the effectorness gradient. Our results illustrate the heterogeneity of T cell responses, furthering our understanding of inflammation.

Cytokines critically control the differentiation and functions of activated naïve and memory T cells. Here the authors show, using multi-omics and single-cell analyses, that naïve and memory T cells exhibit distinct cytokine responses, in which an ‘effectorness gradient’ is depicted by a transcriptional continuum, which shapes the downstream genetic programs.

Uncovering axes of variation among single-cell cancer specimens

While several tools have been developed to map axes of variation among individual cells, no analogous approaches exist for identifying axes of variation among multicellular biospecimens profiled at single-cell resolution. For this purpose, we developed 'phenotypic earth mover's distance' (PhEMD). PhEMD is a general method for embedding a 'manifold of manifolds', in which each datapoint in the higher-level manifold (of biospecimens) represents a collection of points that span a lower-level manifold (of cells). We apply PhEMD to a newly generated drug-screen dataset and demonstrate that PhEMD uncovers axes of cell subpopulational variation among a large set of perturbation conditions. Moreover, we show that PhEMD can be used to infer the phenotypes of biospecimens not directly profiled. Applied to clinical datasets, PhEMD generates a map of the patient-state space that highlights sources of patient-to-patient variation. PhEMD is scalable, compatible with leading batch-effect correction techniques and generalizable to multiple experimental designs.

Heterogeneity and dynamics of active Kras-induced dysplastic lineages from mouse corpus stomach

Dysplasia is considered a key transition state between pre-cancer and cancer in gastric carcinogenesis. However, the cellular or phenotypic heterogeneity and mechanisms of dysplasia progression have not been elucidated. We have established metaplastic and dysplastic organoid lines, derived from Mist1-Kras(G12D) mouse stomach corpus and studied distinct cellular behaviors and characteristics of metaplastic and dysplastic organoids. We also examined functional roles for Kras activation in dysplasia progression using Selumetinib, a MEK inhibitor, which is a downstream mediator of Kras signaling. Here, we report that dysplastic organoids die or show altered cellular behaviors and diminished aggressive behavior in response to MEK inhibition. However, the organoids surviving after MEK inhibition maintain cellular heterogeneity. Two dysplastic stem cell (DSC) populations are also identified in dysplastic cells, which exhibited different clonogenic potentials. Therefore, Kras activation controls cellular dynamics and progression to dysplasia, and DSCs might contribute to cellular heterogeneity in dysplastic cell lineages.

New Insights Into Cholinergic Neuron Diversity

Cholinergic neurons comprise a small population of cells in the striatum but have fundamental roles in fine tuning brain function, and in the etiology of neurological and psychiatric disorders such as Parkinson’s disease (PD) or schizophrenia. The process of developmental cell specification underlying neuronal identity and function is an area of great current interest. There has been significant progress in identifying the developmental origins, commonalities in molecular markers, and physiological properties of the cholinergic neurons. Currently, we are aware of a number of key factors that promote cholinergic fate during development. However, the extent of cholinergic cell diversity is still largely underestimated. New insights into the biological basis of their specification indicate that cholinergic neurons may be far more diverse than previously thought. This review article, highlights the physiological features and the synaptic properties that segregate cholinergic cell subtypes. It provides an accurate picture of cholinergic cell diversity underlying their organization and function in neuronal networks. This review article, also discusses current challenges in deciphering the logic of the cholinergic cell heterogeneity that plays a fundamental role in the control of neural processes in health and disease.

BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes

To fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for identifying cell lineages and bona fide transcriptional signals, it is necessary to combine data from multiple experiments. We present BERMUDA (Batch Effect ReMoval Using Deep Autoencoders), a novel transfer-learning-based method for batch effect correction in scRNA-seq data. BERMUDA effectively combines different batches of scRNA-seq data with vastly different cell population compositions and amplifies biological signals by transferring information among batches. We demonstrate that BERMUDA outperforms existing methods for removing batch effects and distinguishing cell types in multiple simulated and real scRNA-seq datasets.

Neurog3-Independent Methylation Is the Earliest Detectable Mark Distinguishing Pancreatic Progenitor Identity

In the developing pancreas, transient Neurog3-expressing progenitors give rise to four major islet cell types: α, β, δ, and γ; when and how the Neurog3+ cells choose cell fate is unknown. Using single-cell RNA-seq, trajectory analysis, and combinatorial lineage tracing, we showed here that the Neurog3+ cells co-expressing Myt1 (i.e., Myt1+Neurog3+) were biased toward β cell fate, while those not simultaneously expressing Myt1 (Myt1-Neurog3+) favored α fate. Myt1 manipulation only marginally affected α versus β cell specification, suggesting Myt1 as a marker but not determinant for islet-cell-type specification. The Myt1+Neurog3+ cells displayed higher Dnmt1 expression and enhancer methylation at Arx, an α-fate-promoting gene. Inhibiting Dnmts in pancreatic progenitors promoted α cell specification, while Dnmt1 overexpression or Arx enhancer hypermethylation favored β cell production. Moreover, the pancreatic progenitors contained distinct Arx enhancer methylation states without transcriptionally definable sub-populations, a phenotype independent of Neurog3 activity. These data suggest that Neurog3-independent methylation on fate-determining gene enhancers specifies distinct endocrine-cell programs.