Heterogeneous Cell Types Research Articles

MicroRNA-associated DNA Methylation in a Longitudinal, Exploratory Study of Preeclamptic and Normotensive Pregnancy

To evaluate DNA methylation (DNAm) of CpGs annotated to genes for microRNAs (miRNAs) previously implicated in cardiovascular disease (CVD) and preeclampsia (PE) across all trimesters in PE case and normotensive control pregnancies. We leveraged data from an exploratory observational, longitudinal, case-control study of PE cases (n=28) and normotensive controls (n=28) individually matched on self-identified race, pre-pregnancy BMI, smoking status and gestational age at sample collection. The Human miRNA Disease Database was used to generate a candidate list of 172 miRNAs previously implicated in CVD and in PE. CpGs annotated to candidate miRNAs were extracted from epigenome-wide DNAm data collected via Infinium MethylationEPICBeadchip from serial blood samples during the three trimesters of pregnancy. Trimester-specific conditional logistic regression was used to evaluate the association between PE status and miRNA DNAm while controlling for maternal age. Analyses were performed with and without adjustment for cell type heterogeneity (CTH). Raw p-values < 0.05 were considered suggestive for this study. Among the 135 CpGs annotated to candidate miRNAs available for analysis, eight suggestive associations were observed. Specifically, DNAm of cg02999711 (MIR135B), cg26160460 (MIR181A1; MIR181B1), and cg05348084 (MIR376A1; MIR376B) in Trimester 1; cg09494646 (MIR33B), cg09559882 (MIR330), and cg14910227 (MIR495) in Trimester 2; and cg09805692 (MIR451) and cg22420044 (MIR25) in Trimester 3 were associated with PE status. Associations at four CpGs (bolded above) were consistent in both unadjusted and CTH-adjusted analyses. Findings from this exploratory study demonstrate potential differences in miRNA-associated DNAm by PE status. It is possible that aberrant levels of these particular miRNAs in CVD/PE are related to differential DNAm. Follow-up work in larger samples may provide insight into PE pathophysiology, which could inform future clinical interventions.

Resolving placental immune microenvironments with spatial transcriptomics

The placenta separates maternal-fetal antigens and restricts (or permits) microbe residency and vertical transmission. Mechanisms responsible for tolerizing constant antigen challenges remain poorly understood. Single-cell technologies are defining heterogeneity of placental cell types; however, current analyses lack placenta architecture spatial resolution. Thus, we employed spatial transcriptomics paired with H&E to investigate and map placental immune microenvironments.

Estimating cell type composition using isoform expression one gene at a time.

Human tissue samples are often mixtures of heterogeneous cell types, which can confound the analyses of gene expression data derived from such tissues. The cell type composition of a tissue sample may itself be of interest and is needed for proper analysis of differential gene expression. A variety of computational methods have been developed to estimate cell type proportions using gene-level expression data. However, RNA isoforms can also be differentially expressed across cell types, and isoform-level expression could be equally or more informative for determining cell type origin than gene-level expression. We propose a new computational method, IsoDeconvMM, which estimates cell type fractions using isoform-level gene expression data. A novel and useful feature of IsoDeconvMM is that it can estimate cell type proportions using only a single gene, though in practice we recommend aggregating estimates of a few dozen genes to obtain more accurate results. We demonstrate the performance of IsoDeconvMM using a unique data set with cell type-specific RNA-seq data across more than 135 individuals. This data set allows us to evaluate different methods given the biological variation of cell type-specific gene expression data across individuals. We further complement this analysis with additionalsimulations.

Location-Dependent DNA Methylation Signatures in a Clonal Invasive Crayfish.

DNA methylation is an important epigenetic modification that has been repeatedly implied in organismal adaptation. However, many previous studies that have linked DNA methylation patterns to environmental parameters have been limited by confounding factors, such as cell-type heterogeneity and genetic variation. In this study, we analyzed DNA methylation variation in marbled crayfish, a clonal and invasive freshwater crayfish that is characterized by a largely tissue-invariant methylome and negligible genetic variation. Using a capture-based subgenome bisulfite sequencing approach that covers a small, variably methylated portion of the marbled crayfish genome, we identified specific and highly localized DNA methylation signatures for specimens from geographically and ecologically distinct wild populations. These results were replicated both biologically and technically by re-sampling at different time points and by using independent methodology. Finally, we show specific methylation signatures for laboratory animals and for laboratory animals that were reared at a lower temperature. Our results thus demonstrate the existence of context-dependent DNA methylation signatures in a clonal animal.

Characterization of cerebrospinal fluid DNA methylation age during the acute recovery period following aneurysmal subarachnoid hemorrhage

BackgroundBiological aging may occur at different rates than chronological aging due to genetic, social, and environmental factors. DNA methylation (DNAm) age is thought to be a reliable measure of accelerated biological aging which has been linked to an array of poor health outcomes. Given the importance of chronological age in recovery following aneurysmal subarachnoid hemorrhage (aSAH), a type of stroke, DNAm age may also be an important biomarker of outcomes, further improving predictive models. Cerebrospinal fluid (CSF) is a unique tissue representing the local central nervous system environment post-aSAH. However, the validity of CSF DNAm age is unknown, and it is unclear which epigenetic clock is ideal to compute CSF DNAm age, particularly given changes in cell type heterogeneity (CTH) during the acute recovery period. Further, the stability of DNAm age post-aSAH, specifically, has not been examined and may improve our understanding of patient recovery post-aSAH. Therefore, the purpose of this study was to characterize CSF DNAm age over 14 days post-aSAH using four epigenetic clocks.ResultsGenome-wide DNAm data were available for two tissues: (1) CSF for N = 273 participants with serial sampling over 14 days post-aSAH (N = 850 samples) and (2) blood for a subset of n = 72 participants at one time point post-aSAH. DNAm age was calculated using the Horvath, Hannum, Levine, and “Improved Precision” (Zhang) epigenetic clocks. “Age acceleration” was computed as the residuals of DNAm age regressed on chronological age both with and without correcting for CTH. Using scatterplots, Pearson correlations, and group-based trajectory analysis, we examined the relationships between CSF DNAm age and chronological age, the concordance between DNAm ages calculated from CSF versus blood, and the stability (i.e., trajectories) of CSF DNAm age acceleration over time during recovery from aSAH. We observed moderate to strong correlations between CSF DNAm age and chronological age (R = 0.66 [Levine] to R = 0.97 [Zhang]), moderate to strong correlations between DNAm age in CSF versus blood (R = 0.69 [Levine] to R = 0.98 [Zhang]), and stable CSF age acceleration trajectories over 14 days post-aSAH in the Horvath and Zhang clocks (unadjusted for CTH), as well as the Hannum clock (adjusted for CTH).ConclusionsCSF DNAm age was generally stable post-aSAH. Although correlated, CSF DNAm age differs from blood DNAm age in the Horvath, Hannum, and Levine clocks, but not in the Zhang clock. Taken together, our results suggest that, of the clocks examined here, the Zhang clock is the most robust to CTH and is recommended for use in complex tissues such as CSF.

Integrated single-cell transcriptome analysis reveals heterogeneity of esophageal squamous cell carcinoma microenvironment

The tumor microenvironment is a highly complex ecosystem of diverse cell types, which shape cancer biology and impact the responsiveness to therapy. Here, we analyze the microenvironment of esophageal squamous cell carcinoma (ESCC) using single-cell transcriptome sequencing in 62,161 cells from blood, adjacent nonmalignant and matched tumor samples from 11 ESCC patients. We uncover heterogeneity in most cell types of the ESCC stroma, particularly in the fibroblast and immune cell compartments. We identify a tumor-specific subset of CST1+ myofibroblasts with prognostic values and potential biological significance. CST1+ myofibroblasts are also highly tumor-specific in other cancer types. Additionally, a subset of antigen-presenting fibroblasts is revealed and validated. Analyses of myeloid and T lymphoid lineages highlight the immunosuppressive nature of the ESCC microenvironment, and identify cancer-specific expression of immune checkpoint inhibitors. This work establishes a rich resource of stromal cell types of the ESCC microenvironment for further understanding of ESCC biology.

Functional heterogeneity of immune defenses in molluscan oysters Crassostrea hongkongensis revealed by high-throughput single-cell transcriptome

Oyster is the worldwide aquaculture molluscan and evolves a complex immune defense system, with hemocytes as the major immune system for its host defense. However, the functional heterogeneity of hemocyte has not been characterized, which markedly hinders our understanding of its defense role. Here, we used the single-cell transcriptome profiling (scRNA-seq), which provides a high-resolution visual insight into its dynamics, to map the hemocyte and assess its heterogeneity in a molluscan oyster Crassostrea hongkongensis. By combining with the cell type specific RNA-seq, thirteen subpopulations belonging to granulocyte, semi-granulocyte, and hyalinocyte were revealed. The granulocytes mainly participated in immune response and autophagy process. Pseudo-temporal ordering of granulocytes identified two different cell-lineages. The hematopoietic transcription factors regulated networks controlling their differentiations were also identified. We further identified one subpopulation of granulocytes in immune activate states with the cell cycle and immune responsive genes expressions, which illustrated the functional heterogeneity of the same cell type. Collectively, our scRNA-seq analysis demonstrated the hemocytes diversity of molluscans. The results are important in our understanding of the immune defense evolution and functional differentiation of hemocytes in Phylum Mollusca.

Identification of Lipid Heterogeneity and Diversity in the Developing Human Brain.

The lipidome is currently understudied but fundamental to life. Within the brain, little is known about cell-type lipid heterogeneity, and even less is known about cell-to-cell lipid diversity because it is difficult to study the lipids within individual cells. Here, we used single-cell mass spectrometry-based protocols to profile the lipidomes of 154 910 single cells across ten individuals consisting of five developmental ages and five brain regions, resulting in a unique lipid atlas available via a web browser of the developing human brain. From these data, we identify differentially expressed lipids across brain structures, cortical areas, and developmental ages. We inferred lipid profiles of several major cell types from this data set and additionally detected putative cell-type specific lipids. This data set will enable further interrogation of the developing human brain lipidome.

Paths and pathways that generate cell-type heterogeneity and developmental progression in hematopoiesis.

Mechanistic studies of Drosophila lymph gland hematopoiesis are limited by the availability of cell-type-specific markers. Using a combination of bulk RNA-Seq of FACS-sorted cells, single-cell RNA-Seq, and genetic dissection, we identify new blood cell subpopulations along a developmental trajectory with multiple paths to mature cell types. This provides functional insights into key developmental processes and signaling pathways. We highlight metabolism as a driver of development, show that graded Pointed expression allows distinct roles in successive developmental steps, and that mature crystal cells specifically express an alternate isoform of Hypoxia-inducible factor (Hif/Sima). Mechanistically, the Musashi-regulated protein Numb facilitates Sima-dependent non-canonical, and inhibits canonical, Notch signaling. Broadly, we find that prior to making a fate choice, a progenitor selects between alternative, biologically relevant, transitory states allowing smooth transitions reflective of combinatorial expressions rather than stepwise binary decisions. Increasingly, this view is gaining support in mammalian hematopoiesis.

Heterogeneity and Molecular Markers for CNS Glial Cells Revealed by Single-Cell Transcriptomics.

Glial cells, including astrocytes, oligodendrocytes, and microglia, are the major components in the central nervous system (CNS). Studies have revealed the heterogeneity of each glial cell type and that they each may play distinct roles in physiological processes and/or neurological diseases. Single-cell sequencing (scRNA-seq) technology developed in recent years has extended our understanding of glial cell heterogeneity from the perspective of transcriptome profiling. This review summarizes the marker genes of major glial cells in the CNS and reveals their heterogeneity in different species, CNS regions, developmental stages, and pathological states (Alzheimer's disease and spinal cord injury), expanding our knowledge of glial cell heterogeneity on both molecular and functional levels.

What's New in Musculoskeletal Basic Science.

What's New in Musculoskeletal Basic Science.

Molecular and Cellular Dynamics of Aortic Aneurysms Revealed by Single-Cell Transcriptomics.

The aorta is highly heterogeneous, containing many different types of cells that perform sophisticated functions to maintain aortic homeostasis. Recently, single-cell RNA sequencing studies have provided substantial new insight into the heterogeneity of vascular cell types, the comprehensive molecular features of each cell type, and the phenotypic interrelationship between these cell populations. This new information has significantly improved our understanding of aortic biology and aneurysms at the molecular and cellular level. Here, we summarize these findings, with a focus on what single-cell RNA sequencing analysis has revealed about cellular heterogeneity, cellular transitions, communications among cell populations, and critical transcription factors in the vascular wall. We also review the information learned from single-cell RNA sequencing that has contributed to our understanding of the pathogenesis of vascular disease, such as the identification of cell types in which aneurysm-related genes and genetic variants function. Finally, we discuss the challenges and future directions of single-cell RNA sequencing applications in studies of aortic biology and diseases.

DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification

BackgroundQuantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data.ResultsWe present DECONbench, a standardized unbiased benchmarking resource, applied to the evaluation of computational methods quantifying cell-type heterogeneity in cancer. DECONbench includes gold standard simulated benchmark datasets, consisting of transcriptome and methylome profiles mimicking pancreatic adenocarcinoma molecular heterogeneity, and a set of baseline deconvolution methods (reference-free algorithms inferring cell-type proportions). DECONbench performs a systematic performance evaluation of each new methodological contribution and provides the possibility to publicly share source code and scoring.ConclusionDECONbench allows continuous submission of new methods in a user-friendly fashion, each novel contribution being automatically compared to the reference baseline methods, which enables crowdsourced benchmarking. DECONbench is designed to serve as a reference platform for the benchmarking of deconvolution methods in the evaluation of cancer heterogeneity. We believe it will contribute to leverage the benchmarking practices in the biomedical and life science communities. DECONbench is hosted on the open source Codalab competition platform. It is freely available at: https://competitions.codalab.org/competitions/27453.

Microdissection of the Bulk Transcriptome at Single-Cell Resolution Reveals Clinical Significance and Myeloid Cells Heterogeneity in Lung Adenocarcinoma.

BackgroundTumor infiltrating myeloid (TIM) cells constitute a vital element of the tumor microenvironment. The cell-type heterogeneity of TIM has yet to be fully investigated.MethodsWe used a time saving approach to generate a single-cell reference matrix, allowing quantification of cell-type proportions and cell-type-specific gene abundances in bulk RNA-seq data.ResultsTwo distinct clusters, MSC1 and MSC2 (MSC subtype) were newly identified in lung adenocarcinoma (LUAD) patients, both significantly associated with overall survival and immune blockade therapy responses. Twenty myeloid cell types were detected. Thirteen of these had distinct enrichment patterns between MSC1 and MSC2. LAMP3+ dendritic cells, being a mature and transportable subtype of dendritic cell that may migrate to lymph nodes, were noted as associated with non-responsiveness to immunotargeted therapy. High infiltration level of IFIT3+ neutrophils was strongly related to the response to immune-targeted therapy and was seen to activate CD8+ T cells, partly through inflammasome activation. The infiltration levels of TIMP1+ macrophages and S100A8+ neutrophils were both significantly associated with poor prognosis. TIMP1+ macrophages were noted to recruit S100A8+ neutrophils via the CXCL5–CXCR2 axes and promote LUAD progression.ConclusionAltogether, we performed virtual microdissection of the bulk transcriptome at single-cell resolution and provided a promising TIM infiltration landscape that may shed new light on the development of immune therapy.

Consensus-based clustering of single cells by reconstructing cell-to-cell dissimilarity.

The development of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) technology has led to great opportunities for the identification of heterogeneous cell types in complex tissues. Clustering algorithms are of great importance to effectively identify different cell types. In addition, the definition of the distance between each two cells is a critical step for most clustering algorithms. In this study, we found that different distance measures have considerably different effects on clustering algorithms. Moreover, there is no specific distance measure that is applicable to all datasets. In this study, we introduce a new single-cell clustering method called SD-h, which generates an applicable distance measure for different kinds of datasets by optimally synthesizing commonly used distance measures. Then, hierarchical clustering is performed based on the new distance measure for more accurate cell-type clustering. SD-h was tested on nine frequently used scRNA-seq datasets and it showed great superiority over almost all the compared leading single-cell clustering algorithms.

Stem/progenitor cells in normal physiology and disease of the pancreas

Though embryonic pancreas progenitors are well characterised, the existence of stem/progenitor cells in the postnatal mammalian pancreas has been long debated, mainly due to contradicting results on regeneration after injury or disease in mice. Despite these controversies, sequencing advancements combined with lineage tracing and organoid technologies indicate that homeostatic and trigger-induced regenerative responses in mice could occur. The presence of putative progenitor cells in the adult pancreas has been proposed during homeostasis and upon different stress challenges such as inflammation, tissue damage and oncogenic stress. More recently, single cell transcriptomics has revealed a remarkable heterogeneity in all pancreas cell types, with some cells showing the signature of potential progenitors. In this review we provide an overview on embryonic and putative adult pancreas progenitors in homeostasis and disease, with special emphasis on in vitro culture systems and scRNA-seq technology as tools to address the progenitor nature of different pancreatic cells.

Deep Learning of Histopathology Images at the Single Cell Level.

The tumor immune microenvironment (TIME) encompasses many heterogeneous cell types that engage in extensive crosstalk among the cancer, immune, and stromal components. The spatial organization of these different cell types in TIME could be used as biomarkers for predicting drug responses, prognosis and metastasis. Recently, deep learning approaches have been widely used for digital histopathology images for cancer diagnoses and prognoses. Furthermore, some recent approaches have attempted to integrate spatial and molecular omics data to better characterize the TIME. In this review we focus on machine learning-based digital histopathology image analysis methods for characterizing tumor ecosystem. In this review, we will consider three different scales of histopathological analyses that machine learning can operate within: whole slide image (WSI)-level, region of interest (ROI)-level, and cell-level. We will systematically review the various machine learning methods in these three scales with a focus on cell-level analysis. We will provide a perspective of workflow on generating cell-level training data sets using immunohistochemistry markers to “weakly-label” the cell types. We will describe some common steps in the workflow of preparing the data, as well as some limitations of this approach. Finally, we will discuss future opportunities of integrating molecular omics data with digital histopathology images for characterizing tumor ecosystem.

Central nervous system tumors and three-dimensional cell biology: Current and future perspectives in modeling.

Central nervous system (CNS) tumors are a variety of distinct neoplasms that present multiple challenges in terms of treatment and prognosis. Glioblastoma, the most common primary tumor in adults, is associated with poor survival and remains one of the least treatable neoplasms. These tumors are highly heterogenous and complex in their nature. Due to this complexity, traditional cell culturing techniques and methods do not provide an ideal recapitulating model for the study of these tumors’ behavior in vivo. Two-dimensional models lack the spatial arrangement, the heterogeneity in cell types, and the microenvironment that play a large role in tumor cell behavior and response to treatment. Recently, scientists have turned towards three-dimensional culturing methods, namely spheroids and organoids, as they have been shown to recapitulate tumors in a more faithful manner to their in vivo counterparts. Moreover, tumor-on-a-chip systems have lately been employed in CNS tumor modeling and have shown great potential in both studying the pathophysiology and therapeutic testing. In this review, we will discuss the current available literature on in vitro three-dimensional culturing models in CNS tumors, in addition to presenting their advantages and current limitations. We will also elaborate on the future implications of these models and their benefit in the clinical setting.

Multi-omics integration in the age of million single-cell data.

An explosion in single-cell technologies has revealed a previously underappreciated heterogeneity of cell types and novel cell-state associations with sex, disease, development and other processes. Starting with transcriptome analyses, single-cell techniques have extended to multi-omics approaches and now enable the simultaneous measurement of data modalities and spatial cellular context. Data are now available for millions of cells, for whole-genome measurements and for multiple modalities. Although analyses of such multimodal datasets have the potential to provide new insights into biological processes that cannot be inferred with a single mode of assay, the integration of very large, complex, multimodal data into biological models and mechanisms represents a considerable challenge. An understanding of the principles of data integration and visualization methods is required to determine what methods are best applied to a particular single-cell dataset. Each class of method has advantages and pitfalls in terms of its ability to achieve various biological goals, including cell-type classification, regulatory network modelling and biological process inference. In choosing a data integration strategy, consideration must be given to whether the multi-omics data are matched (that is, measured on the same cell) or unmatched (that is, measured on different cells) and, more importantly, the overall modelling and visualization goals of the integrated analysis.

A Catalogus Immune Muris of the mouse immune responses to diverse pathogens

Immunomodulation strategies are crucial for several biomedical applications. However, the immune system is highly heterogeneous and its functional responses to infections remains elusive. Indeed, the characterization of immune response particularities to different pathogens is needed to identify immunomodulatory candidates. To address this issue, we compiled a comprehensive map of functional immune cell states of mouse in response to 12 pathogens. To create this atlas, we developed a single-cell-based computational method that partitions heterogeneous cell types into functionally distinct states and simultaneously identifies modules of functionally relevant genes characterizing them. We identified 295 functional states using 114 datasets of six immune cell types, creating a Catalogus Immune Muris. As a result, we found common as well as pathogen-specific functional states and experimentally characterized the function of an unknown macrophage cell state that modulates the response to Salmonella Typhimurium infection. Thus, we expect our Catalogus Immune Muris to be an important resource for studies aiming at discovering new immunomodulatory candidates.