Heterogeneous Cell Types Research Articles

Development of BACE2-IN-1/tranylcypromine-based compounds to induce steroidogenesis-dependent neuroprotection.

Traumatic brain injury (TBI) constitutes a significant burden on global healthcare systems, especially affecting younger populations, where it is a leading cause of disability and mortality. Current treatments for TBI mainly focus on preventing further brain damage and controlling symptoms. However, despite these approaches, several clinical needs remain unmet. Revelations from single-cell RNA sequencing (scRNA-seq) performed to determine cell-type heterogeneity and gene expression changes in brain tissue indicated that brain trauma increases the expression of lysine-specific demethylase 1 (LSD1) and secretase 2 (BACE2). To capitalize on this finding, a medicinal chemistry campaign was conducted to pragmatically insert tranylcypromine, an LSD1 inhibitor, into a carefully designed BACE2 inhibitory template (BACE2-IN-1). Additionally, tranylcypromine was structurally modified to enhance the effects of LSD1 inhibition in TBI. As a result, a tractable neuroprotective agent, BACE2-IN-1/tranylcypromine-based compound 4, was identified, showing potential to maintain Neuro-2a cell survival by alleviating mitochondrial damage after oxidative stress. Compound 4 also restored TBI-mediated inhibition of the cholesterol biosynthetic pathway (mevalonate pathway) and damage of redox metabolism, increasing neuroprotective effects. Furthermore, behavioral assays, including nest-building and cognitive performance tests, demonstrated significant improvement in mice post-TBI following treatment with compound 4. Taken together, the outcomes of this study validate the favorable effects of inhibiting LSD1 and beta-secretase in mitigating mitochondrial stress and promoting neurometabolic recovery in TBI. These findings pave the way for the development of rationally designed inhibitors as promising neuroprotective agents, potentially addressing unmet clinical needs in TBI treatment.

Microglial modulation of neuronal network function and plasticity.

Microglia are the resident immune cells of the central nervous system (CNS), which have been classically viewed as involved in CNS responses to damage and tissue repair. However, microglia are constantly sensing neuronal network activity and changes in the CNS milieu, establishing complex state-dependent microglia-neuron interactions that impact their functions. By doing so, microglia perform a wide range of physiological roles, including brain homeostasis maintenance, control of neural connectivity, network function modulation, as well as functional and morphological plasticity regulation in health and disease. Here, I review recent evidence of the modulations induced by microglia, a highly heterogeneous cell type, on synaptic and intrinsic neuronal properties, as well as on neuronal network patterns during perinatal development and adulthood. The reviewed evidence clearly indicate that microglia are important, if not essential, for brain function and plasticity in both health and disease.

Epigenetic ageing clocks: statistical methods and emerging computational challenges.

Over the past decade, epigenetic clocks have emerged as powerful machine learning tools, not only to estimate chronological and biological age but also to assess the efficacy of anti-ageing, cellular rejuvenation and disease-preventive interventions. However, many computational and statistical challenges remain that limit our understanding, interpretation and application of epigenetic clocks. Here, we review these computational challenges, focusing on interpretation, cell-type heterogeneity and emerging single-cell methods, aiming to provide guidelines for the rigorous construction of interpretable epigenetic clocks at cell-type and single-cell resolution.

A multi-class support vector machine classification model based on 14 microRNAs for forensic body fluid identification

MicroRNAs (miRNAs) are promising biomarkers for forensic body fluid identification owing to their small size, stability against degradation, and differential expression patterns. However, the expression of most body fluid-miRNAs is relative (differentially expressed in certain body fluids) rather than absolute (exclusively expressed in a specific body fluid). Moreover, different body fluids contain heterogeneous cell types, complicating their identification. Therefore, appropriate normalization strategies to eliminate non-biological variations and robust models to interpret expression levels accurately are necessary prerequisites for applying miRNAs in body fluid identification. In this study, the expression stability of six candidate reference genes (RGs) across five body fluids was validated using geNorm, NormFinder, BestKeeper and RankAggreg, and the most suitable combination of RGs (hsa-miR-484 and hsa-miR-191–5p) was identified under our experimental conditions. Subsequently, we systematically evaluated the expression patterns of the 28 most promising body fluid-specific miRNA markers using TaqMan RT-qPCR and selected the optimal combination of markers (12 miRNAs) to establish a multi-class support vector machine (MSVM) classification model. An independent test set (60 samples) was used to validate the accuracy of the proposed classification model, while an additional 30 casework samples were used to assess its robustness. The MSVM model accurately predicted the body fluid origin for almost all (59/60) single-source samples. Moreover, this model demonstrated the capability to identify aged forensic samples and to predict the primary components of mixed stains to a certain extent. In summary, this study presented a miRNA-based MSVM classification model for forensic body fluid identification using the qPCR platform. However, extensive validation, especially inter-laboratory collaborative exercises, is necessary before miRNA can be routinely applied in forensic identification practice.

The Multi-State Epigenetic Pacemaker enables the identification of combinations of factors that influence DNA methylation.

Epigenetic clocks, DNA methylation-based predictive models of chronological age, are often utilized to study aging associated biology. Despite their widespread use, these methods do not account for other factors that also contribute to the variability of DNA methylation data. For example, many CpG sites show strong sex-specific or cell-type-specific patterns that likely impact the predictions of epigenetic age. To overcome these limitations, we developed a multidimensional extension of the Epigenetic Pacemaker, the Multi-state Epigenetic Pacemaker (MSEPM). We show that the MSEPM is capable of accurately modeling multiple methylation-associated factors simultaneously, while also providing site-specific models that describe the per site relationship between methylation and these factors. We utilized the MSEPM with a large aggregate cohort of blood methylation data to construct models of the effects of age-, sex-, and cell-type heterogeneity on DNA methylation. We found that these models capture a large faction of the variability at thousands of DNA methylation sites. Moreover, this approach allows us to identify sites that are primarily affected by aging and no other factors. An analysis of these sites reveals that those that lose methylation over time are enriched for CTCF transcription factor chip peaks, while those that gain methylation over time are associated with bivalent promoters of genes that are not expressed in blood. These observations suggest mechanisms that underlie age-associated methylation changes and suggest that age-associated increases in methylation may not have strong functional consequences on cell states. In conclusion, the MSEPM is capable of accurately modeling multiple methylation-associated factors, and the models produced can illuminate site-specific combinations of factors that affect methylation dynamics.

STEM-14. INTEGRATED ANALYSIS OF GLIOBLASTOMA PROGENITOR SUBTYPES REVEALS A TUMOR-DERIVED STEM CELL POPULATION WITH NEURAL AND VASCULAR POTENTIAL

Abstract Glioblastoma (GBM) exhibits a vast heterogeneity in cell types and states. Recent research highlights the reactivation of neurodevelopmental programs in GBM, contributing to this heterogeneity and, ultimately, therapy resistance. To characterize the landscape of progenitor cell types in human primary GBM, we compiled a meta-atlas of single-cell transcriptomes from seven published studies. Using a novel bioinformatic method for gene program analysis, we derived GBM gene expression meta-modules represented across multiple tumors. Our analyses validated previously described GBM gene programs and revealed novel tumor progenitor subtypes. Among these subtypes, we identified a tumor-derived stem cell population exhibiting a mixed vascular identity and transcriptional programs reminiscent of neural stem cells, which we identified as a putative neurovascular progenitor (NVP). To our knowledge, this population has not been previously described in GBM. We validated the existence of NVPs in situ using immunofluorescence from primary patient GBM samples. We then performed an enrichment for NVPs using fluorescence-activated cell sorting direct from patient tumors, followed by single-cell RNA sequencing. Examination of data from traditional model systems, including gliomaspheres and mouse GBM models, confirmed the preservation of NVP identity across systems. In vivo analysis of NVPs in multiple mouse models of GBM elucidate the influence of NVPs on GBM cell type composition, highlighting their role as multipotent progenitors. Finally, we utilized single-cell lineage barcoding to trace the progeny of patient-derived NVP cells in a novel organoid transplantation model, revealing NVP differentiation into vascular-like and neural-like cells. These data indicate that NVP cells play crucial roles in tumor plasticity and may represent a valuable therapeutic target for future exploration.

TMIC-69. HUMAN ORGANOID TUMOR TRANSPLANTATION IDENTIFIES FUNCTIONAL GLIOBLASTOMA MICROENVIRONMENTAL COMMUNICATION MEDIATED BY PTPRZ1

Abstract Glioblastoma, the most aggressive form of primary brain cancer, is driven by both intrinsic cellular properties and extrinsic factors from the tumor microenvironment (TME). Here, we leverage our novel human organoid tumor transplantation (HOTT) system to explore how external cues modulate glioblastoma cell type specification, heterogeneity, and migration. Comparing the single-cell transcriptomes of HOTT and matched primary tumors, we found that HOTT recapitulates not only core features of major patient tumor cell types but also those of peritumor non-neoplastic cell types. This enables us to explore tumor-TME interactions and identify PTPRZ1, a receptor tyrosine phosphatase implicated in brain tumor migration, as a key mediator in intercellular communication. This finding was further confirmed by interrogation of published datasets from patients containing non-neoplastic microenvironmental cells. PTPRZ1 activation, resulting from the reactivation of developmental programs in both the tumor and its TME, highlights the relevance of the organoid system for modeling the GBM microenvironment. Moreover, HOTT uniquely allows for perturbations in either patient tumors or their microenvironments, or both, to verify PTPRZ1 function. Tumor knockdown of PTPRZ1 confirmed its role in promoting tumor migration and maintaining progenitor identity. Surprisingly, environmental PTPRZ1 knockdown in human cortical organoids before tumor transplantation inhibited tumor migration and promoted differentiation, even without direct tumor manipulation. Overall, this fundamental result demonstrates that a single treatment delivered to the tumor and its TME can exert opposite effects on the tumor. Consequently, it underscores the necessity of studying human glioblastoma within a human microenvironment context, such as HOTT, especially when evaluating therapeutic efficacy in GBM, as effective treatments must address both the tumor and its complex TME, rather than the tumor alone.

Considerations for Cell Type Heterogeneity in Pediatric Salivary DNA Methylation Analyses: Comparison of Reference Panels & Stratification by Estimated Cell Type Proportion.

Saliva is a widely used sample in epigenetic research with children due to its non-invasive nature. Since DNA methylation (DNAm) profile is cell type (CT) specific, salivary DNAm associations with exposures may be influenced by CT compositions, which is highly variable in saliva as it contains immune and buccal epithelial cells (BEC). Reference-based CT deconvolution and statistically adjusting estimated CT in DNAm analyses have become an increasingly common practice. However, careful examinations of how different reference panels may affect DNAm results and alternative approaches (e.g., stratification) are lacking. To scrutinize the best analytical strategies on pediatric salivary DNAm, the current study used 529 salivary DNAm samples obtained from children (mean age = 7.26 years, SD = 0.26 years) in the Family Life Project. Our results showed higher estimated CT variability with child than adult reference panels and highlighted the impact of these estimated CT discrepancies on DNAm associations with social variables (socioeconomic status). Stratifying salivary DNAm samples by BEC proportions detected a larger number of significant associations with biological variables (sex) and tissue-specific effect with cotinine level, a tobacco smoke-exposure biomarker. We provide analytical recommendations for future epigenetic research involving pediatric saliva samples.

Prenatal exposures and cell type proportions are main drivers of FKBP5 DNA methylation in maltreated and non-maltreated children

DNA methylation in peripheral tissues may be a relevant biomarker of risk for developing mental disorders after exposure to early life adversity. Genes involved in HPA axis regulation, such as FKBP5, might play a key role. In this study, we aimed to identify the main drivers of salivary FKBP5 methylation in a cohort of 162 maltreated and non-maltreated children aged 3–5 years at two measurement timepoints. We combined data from a targeted bisulfite sequencing approach for fine-mapping 49 CpGs in regulatory regions of FKBP5 and epigenetic scores for exposure to alcohol, cigarette smoke, and glucocorticoids derived from the EPICv1 microarray.Most variability of methylation in the FKBP5 locus was explained by estimated cell type proportions as well as epigenetic exposure scores, most prominently by the glucocorticoid exposure score. While not surviving correction for multiple testing, we replicated previously reported associations of FKBP5 methylation with CM. We also detected synergistic effects of both rs1360780 genotype and the glucocorticoid exposure score on FKBP5 hypomethylation. These effects were identified in the 3′TAD, a distal regulatory region of FKBP5 which is not extensively covered in Illumina arrays, emphasizing the need for fine mapping approaches. Additionally, the epigenetic glucocorticoid exposure score was associated with childhood maltreatment, maternal mental disorders, and pregnancy complications, thereby highlighting the role of glucocorticoid signaling in the epigenetic consequences of early adversity.These results underscore the need to assess cell type heterogeneity in targeted assessments of DNA methylation and show the impact of exposures beyond just childhood maltreatment such as glucocorticoid exposure.

The transcriptomic landscape of spinal V1 interneurons reveals a role for En1 in specific elements of motor output.

Neural circuits in the spinal cord are composed of diverse sets of interneurons that play crucial roles in shaping motor output. Despite progress in revealing the cellular architecture of the spinal cord, the extent of cell type heterogeneity within interneuron populations remains unclear. Here, we present a single-nucleus transcriptomic atlas of spinal V1 interneurons across postnatal development. We find that the core molecular taxonomy distinguishing neonatal V1 interneurons perdures into adulthood, suggesting conservation of function across development. Moreover, we identify a key role for En1, a transcription factor that marks the V1 population, in specifying one unique subset of V1Pou6f2 interneurons. Loss of En1 selectively disrupts the frequency of rhythmic locomotor output but does not disrupt flexion/extension limb movement. Beyond serving as a molecular resource for this neuronal population, our study highlights how deep neuronal profiling provides an entry point for functional studies of specialized cell types in motor output.

Prognosis prediction via histological evaluation of cellular heterogeneity in glioblastoma

Glioblastomas (GBMs) are the most aggressive types of central nervous system tumors. Although certain genomic alterations have been identified as prognostic biomarkers of GBMs, the histomorphological features that predict their prognosis remain elusive. In this study, following an integrative diagnosis of 227 GBMs based on the 2021 World Health Organization classification system, the cases were histologically fractionated by cellular variations and abundance to evaluate the relationship between cellular heterogeneity and prognosis in combination with O-6-methylguanine-DNA methyltransferase gene promoter methylation (mMGMTp) status. GBMs comprised four major cell types: astrocytic, pleomorphic, gemistocytic, and rhabdoid cells. t-distributed stochastic neighbor embedding analysis using the histological abundance of heterogeneous cell types identified two distinct groups with significantly different prognoses. In individual cell component analysis, the abundance of gemistocytes showed a significantly favorable prognosis but confounding to mMGMTp status. Conversely, the abundance of epithelioid cells was correlated with the unfavorable prognosis. Linear model analysis showed the favorable prognostic utility of quantifying gemistocytic and epithelioid cells, independent of mMGMTp. The evaluation of GBM cell histomorphological heterogeneity is more effective for prognosis prediction in combination with mMGMTp analysis, indicating that histomorphological analysis is a practical and useful prognostication tool in an integrative diagnosis of GBMs.

Unveiling islet heterogeneity using an automated microfluidic imaging system

Islets of Langerhans are a therapeutic target for diabetes and prediabetes. Measurements of therapeutic inhibitory or excitatory concentrations are often performed using large groups of islets, however, the population heterogeneity cannot be observed when examining the ensemble response. Normal islet function and islet response to therapeutic treatment can be affected by islet heterogeneity, influencing the progression of diabetes mellitus. To identify heterogeneity in an islet population, we developed a simple microfluidic device capable of delivering a stimulant to four independent chambers, allowing measurements of individual responses from a population of 20–25 islets. The device enabled accurate delivery of the same stimulant concentration to all four chambers, with an error <1% between chambers. To demonstrate the capability of this system, ensemble and individual EC/IC\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{50}$$\\end{document} measurements of glucose and diazoxide were performed on murine islets. Results showed little heterogeneity of glucose EC\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{50}$$\\end{document} values with all 21 islets within ± 0.6 mM of the ensemble value of 7.4 mM. In contrast, application of diazoxide to 24 islets in the presence of 20 mM glucose resulted in 37% of islets having an IC\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{50}$$\\end{document} greater than 10% from the ensemble value of 10.2 μM. The simple system developed here is amenable to further studies on islet heterogeneity, and is applicable to investigate heterogeneity in other cell types.

Single Cell VDJ Sequencing of Normal and Malignant B and T Cells.

Recent developments in single cell sequencing technologies enable researchers to examine heterogeneity of cell types and subclusters even deeper. First assays were only available for transcriptome analysis of up to 10,000 cells, but nowadays up to 60,000 cells or even more can be analyzed. Whereas initially only analysis of mRNA expression was possible, currently single cell methods multiplied, with extension of assays for examination of surface molecule expression, DNA accessibility (ATAC-seq), antigen specificity, and B or T cell receptor repertoires. Also, spatial transcriptomics or CRISPR screenings, augmenting classical CRISPR/Cas9 screens by combining them with transcriptomic data at single cell level, can be evaluated. The composition of B and T cell clones-of malignant cells in lymphomas and leukemia, as well as of infiltrating B or T cell clones in other types of cancer-is especially important in tumor research, as these clones may give valuable hints for tumor development and control. This chapter presents detailed methods for implementation and analysis of single cell B and/or T cell receptor repertoire sequencing on the Chromium system from 10× Genomics and the Rhapsody™ system from BD Bioscience.

ScTWAS Atlas: an integrative knowledgebase of single-cell transcriptome-wide association studies.

Single-cell transcriptome-wide association studies (scTWAS) is a new method for conducting TWAS analysis at the cellular level to identify gene-trait associations with higher precision. This approach helps overcome the challenge of interpreting cell-type heterogeneity in traditional TWAS results. As the field of scTWAS rapidly advances, there is a growing need for additional database platforms to integrate this wealth of data and knowledge effectively. To address this gap, we present scTWAS Atlas (https://ngdc.cncb.ac.cn/sctwas/), a comprehensive database of scTWAS information integrating literature curation and data analysis. The current version of scTWAS Atlas amasses 2,765,211 associations encompassing 34 traits, 30 cell types, 9 cell conditions and 16,470 genes. The database features visualization tools, including an interactive knowledge graph that integrates single-cell expression quantitative trait loci (sc-eQTL) and scTWAS associations to build a multi-omics level regulatory network at the cellular level. Additionally, scTWAS Atlas facilitates cross-cell-type analysis, highlighting cell-type-specific and shared TWAS genes. The database is designed with user-friendly interfaces and allows for easy browsing, searching, and downloading of relevant information. Overall, scTWAS Atlas is instrumental in exploring the genetic regulatory mechanisms at the cellular level and shedding light on the role of various cell types in biological processes, offering novel insights for human health research.

Elucidation of Medusozoan (Jellyfish) Venom Constituent Activities Using Constellation Pharmacology.

Within the phylum Cnidaria, sea anemones (class Anthozoa) express a rich diversity of ion-channel peptide modulators with biomedical applications, but corollary discoveries from jellyfish (subphylum Medusozoa) are lacking. To bridge this gap, bioactivities of previously unexplored proteinaceous and small molecular weight (~15 kDa to 5 kDa) venom components were assessed in a mouse dorsal root ganglia (DRG) high-content calcium-imaging assay, known as constellation pharmacology. While the addition of crude venom led to nonspecific cell death and Fura-2 signal leakage due to pore-forming activity, purified small molecular weight fractions of venom demonstrated three main, concentration-dependent and reversible effects on defined heterogeneous cell types found in the primary cultures of mouse DRG. These three phenotypic responses are herein referred to as phenotype A, B and C: excitatory amplification (A) or inhibition (B) of KCl-induced calcium signals, and test compound-induced disturbances to baseline calcium levels (C). Most notably, certain Alatina alata venom fractions showed phenotype A effects in all DRG neurons; Physalia physalis and Chironex fleckeri fractions predominantly showed phenotype B effects in small- and medium-diameter neurons. Finally, specific Physalia physalis and Alatina alata venom components induced direct excitatory responses (phenotype C) in glial cells. These findings demonstrate a diversity of neuroactive compounds in jellyfish venom potentially targeting a constellation of ion channels and ligand-gated receptors with broad physiological implications.

Evidence of apoptosis as an early event leading to cyclophosphamide-induced primordial follicle depletion in a prepubertal mouse model.

The mechanisms leading to ovarian primordial follicle depletion following gonadotoxic chemotherapy with cyclophosphamide and other cytotoxic drugs are currently understood through two main explanatory theories: apoptosis and over-activation. Discrepancies between the findings of different studies investigating these mechanisms do not allow to reach a firm conclusion. The heterogeneity of cell types in ovaries and their different degrees of sensitivity to damage, cell-cell interactions, periodical follicle profile differences, model age-dependent differences, and differences of exposure durations of tested drugs may partially explain the discrepancies among studies. This study used intact prepubertal mice ovaries in culture as study model, in which most follicles are primordial follicles. Histological and transcriptional analyses of ovaries exposed to the active metabolite of cyclophosphamide 4-hydroperoxycyclophosphamide (4-HC) were carried out via a time-course experiment at 8, 24, 48, and 72 h. 4-HC treated ovaries showed a significant decrease in primordial follicle density at 24 h, along with active DNA damage (TUNEL) and overexpressed apoptosis signals (cleaved-poly ADP ribose polymerase in immunohistochemistry and western blotting). Meanwhile 4-HC treatment significantly up-regulated H2ax, Casp 6, Casp 8, Noxa, and Bax in ovaries, and up-regulated Puma in primordial follicles (FISH). Our results indicated that cyclophosphamide-induced acute ovarian primordial follicle depletion was mainly related to apoptotic pathways. No evidence of follicle activation was found, neither through changes in the expression of related genes to follicle activation nor in the density of growing follicles. Further validation at protein level in 4-HC-treated prepubertal mice ovaries at 24 h confirmed these observations.

Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.

Assay for Transposase-Accessible Chromatin sequencing (ATAC-Seq) is a widely used technique to explore gene regulatory mechanisms. For most ATAC-Seq data from healthy and diseased tissues such as tumors, chromatin accessibility measurement represents a mixed signal from multiple cell types. In this work, we derive reliable chromatin accessibility marker peaks and reference profiles for most non-malignant cell types frequently observed in the microenvironment of human tumors. We then integrate these data into the EPIC deconvolution framework (Racle et al., 2017) to quantify cell-type heterogeneity in bulk ATAC-Seq data. Our EPIC-ATAC tool accurately predicts non-malignant and malignant cell fractions in tumor samples. When applied to a human breast cancer cohort, EPIC-ATAC accurately infers the immune contexture of the main breast cancer subtypes.

Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data

Assay for Transposase-Accessible Chromatin sequencing (ATAC-Seq) is a widely used technique to explore gene regulatory mechanisms. For most ATAC-Seq data from healthy and diseased tissues such as tumors, chromatin accessibility measurement represents a mixed signal from multiple cell types. In this work, we derive reliable chromatin accessibility marker peaks and reference profiles for most non-malignant cell types frequently observed in the microenvironment of human tumors. We then integrate these data into the EPIC deconvolution framework (Racle et al., 2017) to quantify cell-type heterogeneity in bulk ATAC-Seq data. Our EPIC-ATAC tool accurately predicts non-malignant and malignant cell fractions in tumor samples. When applied to a human breast cancer cohort, EPIC-ATAC accurately infers the immune contexture of the main breast cancer subtypes.

Spatial Transcriptomics of the Respiratory System.

Over the last decade, single-cell genomics has revealed remarkable heterogeneity and plasticity of cell types in the lungs and airways. The challenge now is to understand how these cell types interact in three-dimensional space to perform lung functions, facilitating airflow and gas exchange while simultaneously providing barrier function to avoid infection. An explosion in novel spatially resolved gene expression technologies, coupled with computational tools that harness machine learning and deep learning, now promise to address this challenge. Here, we review the most commonly used spatial analysis workflows, highlighting their advantages and limitations, and outline recent developments in machine learning and artificial intelligence that will augment how we interpret spatial data. Together these technologies have the potential to transform our understanding of the respiratory system in health and disease, and we showcase studies in lung development, COVID-19, lung cancer, and fibrosis where spatially resolved transcriptomics is already providing novel insights.

Deconvolution from bulk gene expression by leveraging sample-wise and gene-wise similarities and single-cell RNA-seq data

BackgroundThe widely adopted bulk RNA-seq measures the gene expression average of cells, masking cell type heterogeneity, which confounds downstream analyses. Therefore, identifying the cellular composition and cell type-specific gene expression profiles (GEPs) facilitates the study of the underlying mechanisms of various biological processes. Although single-cell RNA-seq focuses on cell type heterogeneity in gene expression, it requires specialized and expensive resources and currently is not practical for a large number of samples or a routine clinical setting. Recently, computational deconvolution methodologies have been developed, while many of them only estimate cell type composition or cell type-specific GEPs by requiring the other as input. The development of more accurate deconvolution methods to infer cell type abundance and cell type-specific GEPs is still essential.ResultsWe propose a new deconvolution algorithm, DSSC, which infers cell type-specific gene expression and cell type proportions of heterogeneous samples simultaneously by leveraging gene-gene and sample-sample similarities in bulk expression and single-cell RNA-seq data. Through comparisons with the other existing methods, we demonstrate that DSSC is effective in inferring both cell type proportions and cell type-specific GEPs across simulated pseudo-bulk data (including intra-dataset and inter-dataset simulations) and experimental bulk data (including mixture data and real experimental data). DSSC shows robustness to the change of marker gene number and sample size and also has cost and time efficiencies.ConclusionsDSSC provides a practical and promising alternative to the experimental techniques to characterize cellular composition and heterogeneity in the gene expression of heterogeneous samples.