Trajectory-based differential expression analysis for single-cell sequencing data

The HSP90-MYC-CDK9 network drives therapeutic resistance in mantle cell lymphoma

Brexucabtagene autoleucel CAR-T therapy is highly efficacious in overcoming resistance to Bruton's tyrosine kinase inhibitors (BTKi) in mantle cell lymphoma. However, many patients relapse post CAR-T therapy with dismal outcomes. To dissect the underlying mechanisms of sequential resistance to BTKi and CAR-T therapy, we performed single-cell RNA sequencing analysis for 66 samples from 25 patients treated with BTKi and/or CAR-T therapy and conducted in-depth bioinformatics™ analysis. Our analysis revealed that MYC activity progressively increased with sequential resistance. HSP90AB1 (Heat shock protein 90 alpha family class B member 1), a MYC target, was identified as early driver of CAR-T resistance. CDK9 (Cyclin-dependent kinase 9), another MYC target, was significantly upregulated in Dual-R samples. Both HSP90AB1 and CDK9 expression were correlated with MYC activity levels. Pharmaceutical co-targeting of HSP90 and CDK9 synergistically diminished MYC activity, leading to potent anti-MCL activity. Collectively, our study revealed that HSP90-MYC-CDK9 network is the primary driving force of therapeutic resistance.

A conserved transcriptional program for MAIT cells across mammalian evolution

Mucosal-associated invariant T (MAIT) cells harbor evolutionarily conserved TCRs, suggesting important functions. As human and mouse MAIT functional programs appear distinct, the evolutionarily conserved MAIT functional features remain unidentified. Using species-specific tetramers coupled to single-cell RNA sequencing, we characterized MAIT cell development in six species spanning 110 million years of evolution. Cross-species analyses revealed conserved transcriptional events underlying MAIT cell maturation, marked by ZBTB16 induction in all species. MAIT cells in human, sheep, cattle, and opossum acquired a shared type-1/17 transcriptional program, reflecting ancestral features. This program was also acquired by human iNKT cells, indicating common differentiation for innate-like T cells. Distinct type-1 and type-17 MAIT subsets developed in rodents, including pet mice and genetically diverse mouse strains. However, MAIT cells further matured in mouse intestines to acquire a remarkably conserved program characterized by concomitant expression of type-1, type-17, cytotoxicity, and tissue-repair genes. Altogether, the study provides a unifying view of the transcriptional features of innate-like T cells across evolution.

Quantifying the clusterness and trajectoriness of single-cell RNA-seq data

Among existing computational algorithms for single-cell RNA-seq analysis, clustering and trajectory inference are two major types of analysis that are routinely applied. For a given dataset, clustering and trajectory inference can generate vastly different visualizations that lead to very different interpretations of the data. To address this issue, we propose multiple scores to quantify the "clusterness" and "trajectoriness" of single-cell RNA-seq data, in other words, whether the data looks like a collection of distinct clusters or a continuum of progression trajectory. The scores we introduce are based on pairwise distance distribution, persistent homology, vector magnitude, Ripley's K, and degrees of connectivity. Using simulated datasets, we demonstrate that the proposed scores are able to effectively differentiate between cluster-like data and trajectory-like data. Using real single-cell RNA-seq datasets, we demonstrate the scores can serve as indicators of whether clustering analysis or trajectory inference is a more appropriate choice for biological interpretation of the data.

Single-cell RNA sequencing to understand host-virus interactions

Single-cell RNA sequencing (scRNA-seq) has allowed for the profiling of host and virus transcripts and host-virus interactions at single-cell resolution. This review summarizes the existing scRNA-seq technologies together with their strengths and weaknesses. The applications of scRNA-seq in various virological studies are discussed in depth, which broaden the understanding of the immune atlas, host-virus interactions, and immune repertoire. scRNA-seq can be widely used for virology in the near future to better understand the pathogenic mechanisms and discover more effective therapeutic strategies.

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.

Representing and extracting knowledge from single-cell data

Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.

Tracking early mammalian organogenesis - prediction and validation of differentiation trajectories at whole organism scale

Early organogenesis represents a key step in animal development, during which pluripotent cells diversify to initiate organ formation. Here, we sampled 300,000 single-cell transcriptomes from mouse embryos between E8.5 and E9.5 in 6-h intervals and combined this new dataset with our previous atlas (E6.5-E8.5) to produce a densely sampled timecourse of >400,000 cells from early gastrulation to organogenesis. Computational lineage reconstruction identified complex waves of blood and endothelial development, including a new programme for somite-derived endothelium. We also dissected the E7.5 primitive streak into four adjacent regions, performed scRNA-seq and predicted cell fates computationally. Finally, we defined developmental state/fate relationships by combining orthotopic grafting, microscopic analysis and scRNA-seq to transcriptionally determine cell fates of grafted primitive streak regions after 24 h of in vitro embryo culture. Experimentally determined fate outcomes were in good agreement with computationally predicted fates, demonstrating how classical grafting experiments can be revisited to establish high-resolution cell state/fate relationships. Such interdisciplinary approaches will benefit future studies in developmental biology and guide the in vitro production of cells for organ regeneration and repair.

Characterization of CD90/Thy-1 as a crucial molecular signature for myogenic differentiation in human urine-derived cells through single-cell RNA sequencing

Human urine-derived cells (UDCs) are primary cultured cells originating from the upper urinary tract and are known to be multipotent. We previously developed MYOD1-transduced UDCs (MYOD1-UDCs) as a model recapitulating the pathogenesis of Duchenne muscular dystrophy (DMD) caused by a lack of dystrophin. MYOD1-UDCs also allow evaluation of the efficacy of exon skipping with antisense oligonucleotides. However, despite the introduction of MYOD1, some MYOD1-UDCs failed to form myotubes, possibly because of heterogeneity among UDCs. Here, we carried out single-cell RNA-sequencing analyses and revealed that CD90/Thy-1 was highly expressed in a limited subpopulation of UDCs with high myogenic potency. Furthermore, CD90-positive MYOD1-UDCs, but not CD90-negative cells, could form myotubes expressing high levels of myosin heavy chain and dystrophin. Notably, overexpression of CD90 in CD90-negative MYOD1-UDCs did not enhance myogenic differentiation, whereas CD90 suppression in CD90-positive UDCs led to decreased myotube formation and decreased myosin heavy chain expression. CD90 may thus contribute to the fusion of single-nucleated MYOD1-UDCs into myotubes but is not crucial for promoting the expression of late muscle regulatory factors. Finally, we confirmed that CD90-positive MYOD1-UDCs derived from patients with DMD were a valuable tool for obtaining a highly reproducible and stable evaluation of exon skipping using antisense oligonucleotide.

Trajectory inference across multiple conditions with condiments

In single-cell RNA sequencing (scRNA-Seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolution. In such dynamic biological systems, cellular states form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-Seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present condiments, a method for the inference and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the cell's conditions along the trajectory's path, we can detect large-scale changes, indicative of differential progression or fate selection. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.

Rapid functional impairment of natural killer cells following tumor entry limits anti-tumor immunity

Immune cell dysfunction within the tumor microenvironment (TME) undermines the control of cancer progression. Established tumors contain phenotypically distinct, tumor-specific natural killer (NK) cells; however, the temporal dynamics, mechanistic underpinning and functional significance of the NK cell compartment remains incompletely understood. Here, we use photo-labeling, combined with longitudinal transcriptomic and cellular analyses, to interrogate the fate of intratumoral NK cells. We reveal that NK cells rapidly lose effector functions and adopt a distinct phenotypic state with features associated with tissue residency. NK cell depletion from established tumors did not alter tumor growth, indicating that intratumoral NK cells cease to actively contribute to anti-tumor responses. IL-15 administration prevented loss of function and improved tumor control, generating intratumoral NK cells with both tissue-residency characteristics and enhanced effector function. Collectively, our data reveals the fate of NK cells after recruitment into tumors and provides insight into how their function may be revived.

Single-cell atlas of human infrapatellar fat pad and synovium implicates APOE signaling in osteoarthritis pathology

The infrapatellar fat pad (IPFP) and synovium play essential roles in maintaining knee joint homeostasis and in the progression of osteoarthritis (OA). The cellular and transcriptional mechanisms regulating the function of these specialized tissues under healthy and diseased conditions are largely unknown. Here, single-cell and single-nuclei RNA sequencing of human IPFP and synovial tissues were performed to elucidate the cellular composition and transcriptional profile. Computational trajectory analysis revealed that dipeptidyl peptidase 4+ mesenchymal cells function as a common progenitor for IPFP adipocytes and synovial lining layer fibroblasts, suggesting that IPFP and synovium represent an integrated tissue unit. OA induced a profibrotic and inflammatory phenotype in mesenchymal lineage cells with biglycan+ intermediate fibroblasts as a major contributor to OA fibrosis. Apolipoprotein E (APOE) signaling from intermediate fibroblasts and macrophages was identified as a critical regulatory factor. Ex vivo incubation of human cartilage with soluble APOE accelerated proteoglycan degeneration. Inhibition of APOE signaling by intra-articular injection of an anti-APOE neutralizing antibody attenuated the progression of collagenase-induced OA in mice, demonstrating a detrimental effect of APOE on cartilage. Our studies provide a framework for designing further therapeutic strategies for OA by describing the cellular and transcriptional landscape of human IPFP and synovium in healthy versus OA joints.

Single-cell transcriptomics of peripheral blood mononuclear cells indicates impaired immune and inflammatory responses in alcohol-associated hepatitis

Alcohol-associated hepatitis (AH) is often diagnosed at advanced stages, and severe AH usually carries poor prognosis and high short-term mortality. In addition, it is challenging to understand the molecular mechanisms of immune dysregulation and inflammation in AH due to the cellular complexity and heterogeneity. Using single-cell RNA sequencing, previous studies found that AH causes dysfunctional innate immune response in monocytes, involving activation of pattern recognition receptors (PRRs) and cytokine signaling pathways. To better understand the coordinated systemic immune response in AH patients, we performed combined single-cell transcriptome, cell-surface protein, and lymphocyte antigen receptor analysis of peripheral blood mononuclear cell (PBMC) samples. Our results showed inflammatory cytokines and chemokines were highly expressed in AH, including IL-2, IL-32, CXC3R1 and CXCL16 in monocytes and NK cells, whereas HLA-DR genes were reduced in monocytes. In addition, we also found altered differentiation of T-helper cells (TH1 and TH17), which could further lead to neutrophil recruitment and macrophage activation. Lastly, our results also suggest impaired NK-cell activation and differentiation in AH with reduced gene expression of KLRC2 and increased gene expression of KLRG1. Our findings indicate different mechanisms may be involved in impaired immune and inflammatory responses for the cellular subtypes of the PBMCs in AH.

Inferring Tree-Shaped Single-Cell Trajectories with Totem

Single-cell transcriptomics allows unbiased characterization of cell heterogeneity in a sample by profiling gene expression at single-cell level. These profiles capture snapshots of transient or steady states in dynamic processes, such as cell cycle, activation, or differentiation, which can be computationally ordered into a "flip-book" of cell development using trajectory inference methods. However, prediction of more complex topology structures, such as multifurcations or trees, remains challenging. In this chapter, we present two user-friendly protocols for inferring tree-shaped single-cell trajectories and pseudotime from single-cell transcriptomics data with Totem. Totem is a trajectory inference method that offers flexibility in inferring both nonlinear and linear trajectories and usability by avoiding the cumbersome fine-tuning of parameters. The QuickStart protocol provides a simple and practical example, whereas the GuidedStart protocol details the analysis step-by-step. Both protocols are demonstrated using a case study of human bone marrow CD34+ cells, allowing the study of the branching of three lineages: erythroid, lymphoid, and myeloid. All the analyses can be fully reproduced in Linux, macOS, and Windows operating systems (amd64 architecture) with >8 Gb of RAM using the provided docker image distributed with notebooks, scripts, and data in Docker Hub (elolab/repro-totem-ti). These materials are shared online under open-source license at https://elolab.github.io/Totem-protocol .

Interpretable trajectory inference with single-cell Linear Adaptive Negative-binomial Expression (scLANE) testing

The rapid proliferation of trajectory inference methods for single-cell RNA-seq data has allowed researchers to investigate complex biological processes by examining underlying gene expression dynamics. After estimating a latent cell ordering, statistical models are used to determine which genes exhibit changes in expression that are significantly associated with progression through the biological trajectory. While a few techniques for performing trajectory differential expression exist, most rely on the flexibility of generalized additive models in order to account for the inherent nonlinearity of changes in gene expression. As such, the results can be difficult to interpret, and biological conclusions often rest on subjective visual inspections of the most dynamic genes. To address this challenge, we propose scLANE testing, which is built around an interpretable generalized linear model and handles nonlinearity with basis splines chosen empirically for each gene. In addition, extensions to estimating equations and mixed models allow for reliable trajectory testing under complex experimental designs. After validating the accuracy of scLANE under several different simulation scenarios, we apply it to a set of diverse biological datasets and display its ability to provide novel biological information when used downstream of both pseudotime and RNA velocity estimation methods.

Overview of Gene Expression Dynamics during Human Oogenesis/Folliculogenesis

The oocyte transcriptome follows a tightly controlled dynamic that leads the oocyte to grow and mature. This succession of distinct transcriptional states determines embryonic development prior to embryonic genome activation. However, these oocyte maternal mRNA regulatory events have yet to be decoded in humans. We reanalyzed human single-oocyte RNA-seq datasets previously published in the literature to decrypt the transcriptomic reshuffles ensuring that the oocyte is fully competent. We applied trajectory analysis (pseudotime) and a meta-analysis and uncovered the fundamental transcriptomic requirements of the oocyte at any moment of oogenesis until reaching the metaphase II stage (MII). We identified a bunch of genes showing significant variation in expression from primordial-to-antral follicle oocyte development and characterized their temporal regulation and their biological relevance. We also revealed the selective regulation of specific transcripts during the germinal vesicle-to-MII transition. Transcripts associated with energy production and mitochondrial functions were extensively downregulated, while those associated with cytoplasmic translation, histone modification, meiotic processes, and RNA processes were conserved. From the genes identified in this study, some appeared as sensitive to environmental factors such as maternal age, polycystic ovary syndrome, cryoconservation, and in vitro maturation. In the future, the atlas of transcriptomic changes described in this study will enable more precise identification of the transcripts responsible for follicular growth and oocyte maturation failures.

scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles

Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) data for novel discoveries. We developed scNAT, a deep learning method that integrates paired scRNA-seq and scTCR-seq data to represent data in a unified latent space for downstream analysis. We demonstrate that scNAT is capable of removing batch effects, and identifying cell clusters and a T cell migration trajectory from blood to cerebrospinal fluid in multiple sclerosis.

Murine trophoblast organoids as a model for trophoblast development and CRISPR-Cas9 screening

The placenta becomes one of the most diversified organs during placental mammal radiation. The main in vitro model for studying mouse trophoblast development is the 2D differentiation model of trophoblast stem cells, which is highly skewed to certain lineages and thus hampers systematic screens. Here, we established culture conditions for the establishment, maintenance, and differentiation of murine trophoblast organoids. Murine trophoblast organoids under the maintenance condition contain stem cell-like populations, whereas differentiated organoids possess various trophoblasts resembling placental ones in vivo. Ablation of Nubpl or Gcm1 in trophoblast organoids recapitulated their deficiency phenotypes in vivo, suggesting that those organoids are valid in vitro models for trophoblast development. Importantly, we performed an efficient CRISPR-Cas9 screening in mouse trophoblast organoids using a focused sgRNA (single guide RNA) library targeting G protein-coupled receptors. Together, our results establish an organoid model to investigate mouse trophoblast development and a practicable approach to performing forward screening in trophoblast lineages.

Inference after latent variable estimation for single-cell RNA sequencing data

In the analysis of single-cell RNA sequencing data, researchers often characterize the variation between cells by estimating a latent variable, such as cell type or pseudotime, representing some aspect of the cell's state. They then test each gene for association with the estimated latent variable. If the same data are used for both of these steps, then standard methods for computing p-values in the second step will fail to achieve statistical guarantees such as Type 1 error control. Furthermore, approaches such as sample splitting that can be applied to solve similar problems in other settings are not applicable in this context. In this article, we introduce count splitting, a flexible framework that allows us to carry out valid inference in this setting, for virtually any latent variable estimation technique and inference approach, under a Poisson assumption. We demonstrate the Type 1 error control and power of count splitting in a simulation study and apply count splitting to a data set of pluripotent stem cells differentiating to cardiomyocytes.

Preleukemic single-cell landscapes reveal mutation-specific mechanisms and gene programs predictive of AML patient outcomes

Acute myeloid leukemia (AML) and myeloid neoplasms develop through acquisition of somatic mutations that confer mutation-specific fitness advantages to hematopoietic stem and progenitor cells. However, our understanding of mutational effects remains limited to the resolution attainable within immunophenotypically and clinically accessible bulk cell populations. To decipher heterogeneous cellular fitness to preleukemic mutational perturbations, we performed single-cell RNA sequencing of eight different mouse models with driver mutations of myeloid malignancies, generating 269,048 single-cell profiles. Our analysis infers mutation-driven perturbations in cell abundance, cellular lineage fate, cellular metabolism, and gene expression at the continuous resolution, pinpointing cell populations with transcriptional alterations associated with differentiation bias. We further develop an 11-gene scoring system (Stem11) on the basis of preleukemic transcriptional signatures that predicts AML patient outcomes. Our results demonstrate that a single-cell-resolution deep characterization of preleukemic biology has the potential to enhance our understanding of AML heterogeneity and inform more effective risk stratification strategies.

Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors

Colorectal cancer exhibits dynamic cellular and genetic heterogeneity during progression from precursor lesions toward malignancy. Analysis of spatial multi-omic data from 31 human colorectal specimens enabled phylogeographic mapping of tumor evolution that revealed individualized progression trajectories and accompanying microenvironmental and clonal alterations. Phylogeographic mapping ordered genetic events, classified tumors by their evolutionary dynamics, and placed clonal regions along global pseudotemporal progression trajectories encompassing the chromosomal instability (CIN+) and hypermutated (HM) pathways. Integrated single-cell and spatial transcriptomic data revealed recurring epithelial programs and infiltrating immune states along progression pseudotime. We discovered an immune exclusion signature (IEX), consisting of extracellular matrix regulators DDR1, TGFBI, PAK4, and DPEP1, that charts with CIN+ tumor progression, is associated with reduced cytotoxic cell infiltration, and shows prognostic value in independent cohorts. This spatial multi-omic atlas provides insights into colorectal tumor-microenvironment co-evolution, serving as a resource for stratification and targeted treatments.

Heterogeneous murine peribiliary glands orchestrate compartmentalized epithelial renewal

The extrahepatic branches of the biliary tree have glands that connect to the surface epithelium through narrow pits. The duct epithelia undergo homeostatic renewal, yet the identity and multiplicity of cells that maintain this tissue is unknown. Using marker-free and targeted clonal fate mapping in mice, we provide evidence that the extrahepatic bile duct is compartmentalized. Pit cholangiocytes of extramural glands renewed the surface epithelium, whereas basally oriented cholangiocytes maintained the gland itself. In contrast, basally positioned cholangiocytes replenished the surface epithelium in mural glands. Single-cell sequencing identified genes enriched in the base and surface epithelial populations, with trajectory analysis showing graded gene expression between these compartments. Epithelia were plastic, changing cellular identity upon fasting and refeeding. Gain of canonical Wnt signaling caused basal cell expansion, gastric chief cell marker expression, and a decrease in surface epithelial markers. Our results identify the cellular hierarchy governing extrahepatic biliary epithelial renewal.

Comparative Analysis of Single-cell and Single-nucleus RNA-sequencing in a Rabbit Model of Retinal Detachment-related Proliferative Vitreoretinopathy

Purpose

Proliferative vitreoretinopathy (PVR) is the most common cause of failure of retinal reattachment surgery, and the molecular changes leading to this aberrant wound healing process are currently unknown. Our ultimate goal is to study PVR pathogenesis by employing single-cell transcriptomics to dissect cellular heterogeneity.

Design

Here we aimed to compare single-cell RNA sequencing (scRNA-seq)  and single-nucleus RNA-sequencing (snRNA-seq) of retinal PVR samples in the rabbit model.

Participants

Unilateral induction of PVR lesions in rabbit eyes with contralateral eyes serving as controls.

Methods

Proliferative vitreoretinopathy was induced unilaterally in Dutch Belted rabbits. At different timepoints after PVR induction, retinas were dissociated into either cells or nuclei suspension and processed for scRNA-seq or snRNA-seq.

Main Outcome Measures

Single cell and nuclei transcriptomic profiles of retinas after PVR induction.

Results

Single-cell RNA sequencing and snRNA-seq were conducted on retinas at 4 hours and 14 days after disease induction. Although the capture rate of unique molecular identifiers and genes were greater in scRNA-seq samples, overall gene expression profiles of individual cell types were highly correlated between scRNA-seq and snRNA-seq. A major disparity between the 2 sequencing modalities was the cell type capture rate, however, with glial cell types overrepresented in scRNA-seq, and inner retinal neurons were enriched by snRNA-seq. Furthermore, fibrotic Müller glia were overrepresented in snRNA-seq samples, whereas reactive Müller glia were overrepresented in scRNA-seq samples. Trajectory analyses were similar between the 2 methods, allowing for the combined analysis of the scRNA-seq and snRNA-seq data sets.

Conclusions

These findings highlight limitations of both scRNA-seq and snRNA-seq analysis and imply that use of both techniques together can more accurately identify transcriptional networks critical for aberrant fibrogenesis in PVR than using either in isolation.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

Single-cell analysis of chromatin accessibility in the adult mouse brain

Recent advances in single-cell technologies have led to the discovery of thousands of brain cell types; however, our understanding of the gene regulatory programs in these cell types is far from complete1-4. Here we report a comprehensive atlas of candidate cis-regulatory DNA elements (cCREs) in the adult mouse brain, generated by analysing chromatin accessibility in 2.3 million individual brain cells from 117 anatomical dissections. The atlas includes approximately 1 million cCREs and their chromatin accessibility across 1,482 distinct brain cell populations, adding over 446,000 cCREs to the most recent such annotation in the mouse genome. The mouse brain cCREs are moderately conserved in the human brain. The mouse-specific cCREs-specifically, those identified from a subset of cortical excitatory neurons-are strongly enriched for transposable elements, suggesting a potential role for transposable elements in the emergence of new regulatory programs and neuronal diversity. Finally, we infer the gene regulatory networks in over 260 subclasses of mouse brain cells and develop deep-learning models to predict the activities of gene regulatory elements in different brain cell types from the DNA sequence alone. Our results provide a resource for the analysis of cell-type-specific gene regulation programs in both mouse and human brains.

Multiomic integration reveals neuronal-extracellular vesicle coordination of gliotic responses in degeneration

In the central nervous system (CNS), including in the retina, neuronal-to-glial communication is critical for maintaining tissue homeostasis including signal transmission, transfer of trophic factors, and in the modulation of inflammation. Extracellular vesicle (EV)-mediated transport of molecular messages to regulate these processes has been suggested as a mechanism by which bidirectional communication between neuronal and glial cells can occur. In this work we employed multiomics integration to investigate the role of EV communication pathways from neurons to glial cells within the CNS, using the mouse retina as a readily accessible representative CNS tissue. Further, using a well-established model of degeneration, we aimed to uncover how dysregulation of homeostatic messaging between neurons and glia via EV can result in retinal and neurodegenerative diseases. EV proteomics, glia microRNA (miRNA) Open Array and small RNA sequencing, and retinal single cell sequencing were performed, with datasets integrated and analysed computationally. Results demonstrated that exogenous transfer of neuronal miRNA to glial cells was mediated by EV and occurred as a targeted response during degeneration to modulate gliotic inflammation. Taken together, our results support a model of neuronal-to-glial communication via EV, which could be harnessed for therapeutic targeting to slow the progression of retinal-, and neuro-degenerations of the CNS.

Elucidating dynamic cell lineages and gene networks in time-course single cell differentiation

Single cell RNA sequencing (scRNA-seq) technologies provide researchers with an unprecedented opportunity to exploit cell heterogeneity. For example, the sequenced cells belong to various cell lineages, which may have different cell fates in stem and progenitor cells. Those cells may differentiate into various mature cell types in a cell differentiation process. To trace the behavior of cell differentiation, researchers reconstruct cell lineages and predict cell fates by ordering cells chronologically into a trajectory with a pseudo-time. However, in scRNA-seq experiments, there are no cell-to-cell correspondences along with the time to reconstruct the cell lineages, which creates a significant challenge for cell lineage tracing and cell fate prediction. Therefore, methods that can accurately reconstruct the dynamic cell lineages and predict cell fates are highly desirable. In this article, we develop an innovative machine-learning framework called Cell Smoothing Transformation (CellST) to elucidate the dynamic cell fate paths and construct gene networks in cell differentiation processes. Unlike the existing methods that construct one single bulk cell trajectory, CellST builds cell trajectories and tracks behaviors for each individual cell. Additionally, CellST can predict cell fates even for less frequent cell types. Based on the individual cell fate trajectories, CellST can further construct dynamic gene networks to model gene-gene relationships along the cell differentiation process and discover critical genes that potentially regulate cells into various mature cell types.

PD-1- CD45RA+ effector-memory CD8 T cells and CXCL10+ macrophages are associated with response to atezolizumab plus bevacizumab in advanced hepatocellular carcinoma

The combination of atezolizumab plus bevacizumab (atezo/bev) has dramatically changed the treatment landscape of advanced HCC (aHCC), achieving durable responses in some patients. Using single-cell transcriptomics, we characterize the intra-tumoural and peripheral immune context of patients with aHCC treated with atezo/bev. Tumours from patients with durable responses are enriched for PDL1+ CXCL10+ macrophages and, based on cell-cell interaction analysis, express high levels of CXCL9/10/11 and are predicted to attract peripheral CXCR3+ CD8+ effector-memory T cells (CD8 TEM) into the tumour. Based on T cell receptor sharing and pseudotime trajectory analysis, we propose that CD8 TEM preferentially differentiate into clonally-expanded PD1- CD45RA+ effector-memory CD8+ T cells (CD8 TEMRA) with pronounced cytotoxicity. In contrast, in non-responders, CD8 TEM remain frozen in their effector-memory state. Finally, in responders, CD8 TEMRA display a high degree of T cell receptor sharing with blood, consistent with their patrolling activity. These findings may help understand the possible mechanisms underlying response to atezo/bev in aHCC.

Maternal blood transcriptome as a sensor of fetal organ maturation at the end of organogenesis in cattle†

Harnessing information from the maternal blood to predict fetal growth is attractive yet scarcely explored in livestock. The objectives were to determine the transcriptomic modifications in maternal blood and fetal liver, gonads, and heart according to fetal weight and to model a molecular signature based on the fetal organs allowing the prediction of fetal weight from the maternal blood transcriptome in cattle. In addition to a contemporaneous maternal blood sample, organ samples were collected from 10 male fetuses at 42 days of gestation for RNA-sequencing. Fetal weight ranged from 1.25 to 1.69 g (mean = 1.44 ± 0.15 g). Clustering data analysis revealed clusters of co-expressed genes positively correlated with fetal weight and enriching ontological terms biologically relevant for the organ. For the heart, the 1346 co-expressed genes were involved in energy generation and protein synthesis. For the gonads, the 1042 co-expressed genes enriched seminiferous tubule development. The 459 co-expressed genes identified in the liver were associated with lipid synthesis and metabolism. Finally, the cluster of 571 co-expressed genes determined in maternal blood enriched oxidative phosphorylation and thermogenesis. Next, data from the fetal organs were used to train a regression model of fetal weight, which was predicted with the maternal blood data. The best prediction was achieved when the model was trained with 35 co-expressed genes overlapping between heart and maternal blood (root-mean-square error = 0.04, R2 = 0.93). In conclusion, linking transcriptomic information from maternal blood with that from the fetal heart unveiled maternal blood as a predictor of fetal development.

A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples

Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many methods have been developed to infer the pseudotemporal trajectories of cells within a biological sample, it remains a challenge to compare pseudotemporal patterns with multiple samples (or replicates) across different experimental conditions. Here, we introduce Lamian, a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. Lamian can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions while adjusting for batch effects, and to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both real scRNA-seq and simulation data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.

Single-cell profiling and zebrafish avatars reveal LGALS1 as immunomodulating target in glioblastoma

Glioblastoma (GBM) remains the most malignant primary brain tumor, with a median survival rarely exceeding 2 years. Tumor heterogeneity and an immunosuppressive microenvironment are key factors contributing to the poor response rates of current therapeutic approaches. GBM-associated macrophages (GAMs) often exhibit immunosuppressive features that promote tumor progression. However, their dynamic interactions with GBM tumor cells remain poorly understood. Here, we used patient-derived GBM stem cell cultures and combined single-cell RNA sequencing of GAM-GBM co-cultures and real-time in vivo monitoring of GAM-GBM interactions in orthotopic zebrafish xenograft models to provide insight into the cellular, molecular, and spatial heterogeneity. Our analyses revealed substantial heterogeneity across GBM patients in GBM-induced GAM polarization and the ability to attract and activate GAMs-features that correlated with patient survival. Differential gene expression analysis, immunohistochemistry on original tumor samples, and knock-out experiments in zebrafish subsequently identified LGALS1 as a primary regulator of immunosuppression. Overall, our work highlights that GAM-GBM interactions can be studied in a clinically relevant way using co-cultures and avatar models, while offering new opportunities to identify promising immune-modulating targets.

Hepatic lipid-associated macrophages mediate the beneficial effects of bariatric surgery against MASH

For patients with obesity and metabolic syndrome, bariatric procedures such as vertical sleeve gastrectomy (VSG) have a clear benefit in ameliorating metabolic dysfunction-associated steatohepatitis (MASH). While the effects of bariatric surgeries have been mainly attributed to nutrient restriction and malabsorption, whether immuno-modulatory mechanisms are involved remains unclear. Here we report that VSG ameliorates MASH progression in a weight loss-independent manner. Single-cell RNA sequencing revealed that hepatic lipid-associated macrophages (LAMs) expressing the triggering receptor expressed on myeloid cells 2 (TREM2) increase their lysosomal activity and repress inflammation in response to VSG. Remarkably, TREM2 deficiency in mice ablates the reparative effects of VSG, suggesting that TREM2 is required for MASH resolution. Mechanistically, TREM2 prevents the inflammatory activation of macrophages and is required for their efferocytotic function. Overall, our findings indicate that bariatric surgery improves MASH through a reparative process driven by hepatic LAMs, providing insights into the mechanisms of disease reversal that may result in new therapies and improved surgical interventions.

A prognostic model of drug tolerant persister-related genes in lung adenocarcinoma based on single cell and bulk RNA sequencing data

Background

Acquired resistance to targeted drugs is a major challenge in cancer. The drug-tolerant state has been proposed to be an initial step towards acquisition of real drug-resistance. Drug tolerant persister (DTP) cells are purported to survive during treatment and stay dormant for several years. Single cell sequencing can provide a comprehensive landscape of gene expression in DTP cells, which can facilitate investigation of heterogeneity of a drug tolerant state and identification of new anticancer targets.

Methods

The genetic profiling of DTPs was explored by integrating Gene Expression Omnibus (GEO) datasets, and a prognostic signature of DTP-related genes (DTPRGs) in lung adenocarcinoma of TCGA LUAD cohort was constructed. The scores of infiltrating immune cells were calculated and activity of immune-related pathways was evaluated by single-sample gene set enrichment analysis (ssGSEA). Functional enrichment analysis of the DTPRGs between low- and high-risk groups was performed. Immune cell subtypes and immune-related pathways were analyzed.

Results

An 11-gene panel (MT2A, UBE2S, CLTB, KRT7, IGFBP3, CTSH, NPC2, HMGA1, HNRNPAB, DTYMK, and IHNA) was established. DTPRGs were mainly correlated with nuclear division, chromosome segregation, and cell cycle pathways. Infiltration of immune cells was lower in the high-risk group while the inflammation-promoting and MCH-class I response pathway had higher activity in the high-risk group. A nomogram was generated with prognostic accuracy, further validated using clinical outcomes following therapy with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs).

Discussion

A prognostic model of lung adenocarcinoma based on DTPRGs was constructed. Targeting DTP cells is a potential therapeutic approach to prevent a drug tolerant state.

Cordycepin synergizes with CTLA-4 blockade to remodel the tumor microenvironment for enhanced cancer immunotherapy

The strategy of using immune checkpoint inhibitors (ICIs) has revolutionized cancer treatment, leading to remarkable clinical outcomes. However, certain cancer types and patient demographics continue to face unique challenges. As a result, it is vital to investigate combination therapies that involve ICIs to boost therapeutic efficacy. Cordycepin, an adenosine derivative composed of adenine and pentose, holds immense promise for treating inflammation and cancer. Our recent research has demonstrated that the combined treatment of cordycepin and the anti-CD47 antibody significantly curtails tumor growth and extends the lifespan of tumor-bearing mice. In the current study, we showed that the combination of cordycepin and CTLA-4 blockade had a profound impact on suppressing tumor growth. We utilized the MC38 and CT26 tumor models to evaluate the therapeutic effect of cordycepin, CTLA-4 blockade, and their combined approach. Flow cytometry results unveiled that cordycepin, when combined with CTLA-4 blockade, considerably augmented the presence of tumor-infiltrating CD8+T cells and diminished the population of Foxp3+Tregs within the tumor microenvironment (TME). Additionally, we employed single-cell analysis to examine the TME's reconfiguration upon the combined treatment of anti-CTLA-4 and cordycepin. We observed a significant impact on inhibiting tumor growth and substantially extended survival in tumor-bearing mice. Our data also demonstrated an increased proportion of effector CD8+T cells in the combined treatment group compared to all other groups, while exhausted CD8+T cells diminished in the combined group compared to the anti-CTLA-4 treatment alone. In conclusion, our findings supported the idea that combining cordycepin and CTLA-4 blockade could modify the effector and exhaustion status of CD8+T cells, thereby bolstering CD8+T-cell-mediated anti-tumor immunity in the TME. Collectively, our current study successfully established a combination therapeutic strategy utilizing cordycepin and CTLA-4 blockade. This strategy demonstrated a significant synergistic effect against cancer, highlighting its importance in cancer treatment.

Primary cilia-mediated regulation of microglial secretion in Alzheimer's disease

Alzheimer's disease (AD) is a brain disorder manifested by a gradual decline in cognitive function due to the accumulation of extracellular amyloid plaques, disruptions in neuronal substance transport, and the degeneration of neurons. In affected neurons, incomplete clearance of toxic proteins by neighboring microglia leads to irreversible brain inflammation, for which cellular signaling is poorly understood. Through single-cell transcriptomic analysis, we discovered distinct regional differences in the ability of microglia to clear damaged neurites. Specifically, microglia in the septal region of wild type mice exhibited a transcriptomic signature resembling disease-associated microglia (DAM). These lateral septum (LS)-enriched microglia were associated with dense axonal bundles originating from the hippocampus. Further transcriptomic and proteomic approaches revealed that primary cilia, small hair-like structures found on cells, played a role in the regulation of microglial secretory function. Notably, primary cilia were transiently observed in microglia, and their presence was significantly reduced in microglia from AD mice. We observed significant changes in the secretion and proteomic profiles of the secretome after inhibiting the primary cilia gene intraflagellar transport particle 88 (Ift88) in microglia. Intriguingly, inhibiting primary cilia in the septal microglia of AD mice resulted in the expansion of extracellular amyloid plaques and damage to adjacent neurites. These results indicate that DAM-like microglia are present in the LS, a critical target region for hippocampal nerve bundles, and that the primary ciliary signaling system regulates microglial secretion, affecting extracellular proteostasis. Age-related primary ciliopathy probably contributes to the selective sensitivity of microglia, thereby exacerbating AD. Targeting the primary ciliary signaling system could therefore be a viable strategy for modulating neuroimmune responses in AD treatments.

Multimodal cartography of human lymphopoiesis reveals B and T/NK/ILC lineages are subjected to differential regulation

The developmental cartography of human lymphopoiesis remains incompletely understood. Here, we establish a multimodal map demonstrating that lymphoid specification follows independent direct or stepwise hierarchic routes converging toward the emergence of newly characterized CD117lo multi-lymphoid progenitors (MLPs) that undergo a proliferation arrest before entering the CD127- (NK/ILC/T) or CD127+ (B) lymphoid pathways. While the differentiation of CD127- early lymphoid progenitors is mainly driven by Flt3 signaling, emergence of their CD127+ counterparts is regulated cell-intrinsically and depends exclusively on the divisional history of their upstream precursors, including hematopoietic stem cells. Further, transcriptional mapping of differentiation trajectories reveals that whereas myeloid granulomonocytic lineages follow continuous differentiation pathways, lymphoid trajectories are intrinsically discontinuous and characterized by sequential waves of cell proliferation allowing pre-commitment amplification of lymphoid progenitor pools. Besides identifying new lymphoid specification pathways and regulatory checkpoints, our results demonstrate that NK/ILC/T and B lineages are under fundamentally distinct modes of regulation. (149 words).

Multi-omic profiling of the developing human cerebral cortex at the single-cell level

The cellular complexity of the human brain is established via dynamic changes in gene expression throughout development that is mediated, in part, by the spatiotemporal activity of cis-regulatory elements (CREs). We simultaneously profiled gene expression and chromatin accessibility in 45,549 cortical nuclei across six broad developmental time points from fetus to adult. We identified cell type-specific domains in which chromatin accessibility is highly correlated with gene expression. Differentiation pseudotime trajectory analysis indicates that chromatin accessibility at CREs precedes transcription and that dynamic changes in chromatin structure play a critical role in neuronal lineage commitment. In addition, we mapped cell type-specific and temporally specific genetic loci implicated in neuropsychiatric traits, including schizophrenia and bipolar disorder. Together, our results describe the complex regulation of cell composition at critical stages in lineage determination and shed light on the impact of spatiotemporal alterations in gene expression on neuropsychiatric disease.

Principles and challenges of modeling temporal and spatial omics data

Studies with temporal or spatial resolution are crucial to understand the molecular dynamics and spatial dependencies underlying a biological process or system. With advances in high-throughput omic technologies, time- and space-resolved molecular measurements at scale are increasingly accessible, providing new opportunities to study the role of timing or structure in a wide range of biological questions. At the same time, analyses of the data being generated in the context of spatiotemporal studies entail new challenges that need to be considered, including the need to account for temporal and spatial dependencies and compare them across different scales, biological samples or conditions. In this Review, we provide an overview of common principles and challenges in the analysis of temporal and spatial omics data. We discuss statistical concepts to model temporal and spatial dependencies and highlight opportunities for adapting existing analysis methods to data with temporal and spatial dimensions.

IL-7 receptor signaling drives human B-cell progenitor differentiation and expansion

Although absence of interleukin-7 (IL-7) signaling completely abrogates T and B lymphopoiesis in mice, patients with severe combined immunodeficiency caused by mutations in the IL-7 receptor α chain (IL-7Rα) still generate peripheral blood B cells. Consequently, human B lymphopoiesis has been thought to be independent of IL-7 signaling. Using flow cytometric analysis and single-cell RNA sequencing of bone marrow samples from healthy controls and patients who are IL-7Rα deficient, in combination with in vitro modeling of human B-cell differentiation, we demonstrate that IL-7R signaling plays a crucial role in human B lymphopoiesis. IL-7 drives proliferation and expansion of early B-cell progenitors but not of pre-BII large cells and has a limited role in the prevention of cell death. Furthermore, IL-7 guides cell fate decisions by enhancing the expression of BACH2, EBF1, and PAX5, which jointly orchestrate the specification and commitment of early B-cell progenitors. In line with this observation, early B-cell progenitors of patients with IL-7Rα deficiency still expressed myeloid-specific genes. Collectively, our results unveil a previously unknown role for IL-7 signaling in promoting the B-lymphoid fate and expanding early human B-cell progenitors while defining important differences between mice and humans. Our results have implications for hematopoietic stem cell transplantation strategies in patients with T- B+ severe combined immunodeficiency and provide insights into the role of IL-7R signaling in leukemogenesis.

Multi-OMIC analysis of Huntington disease reveals a neuroprotective astrocyte state

Huntington disease (HD) is an incurable neurodegenerative disease characterized by neuronal loss and astrogliosis. One hallmark of HD is the selective neuronal vulnerability of striatal medium spiny neurons. To date, the underlying mechanisms of this selective vulnerability have not been fully defined. Here, we employed a multi-omic approach including single nucleus RNAseq (snRNAseq), bulk RNAseq, lipidomics, HTT gene CAG repeat length measurements, and multiplexed immunofluorescence on post-mortem brain tissue from multiple brain regions of HD and control donors. We defined a signature of genes that is driven by CAG repeat length and found it enriched in astrocytic and microglial genes. Moreover, weighted gene correlation network analysis showed loss of connectivity of astrocytic and microglial modules in HD and identified modules that correlated with CAG-repeat length which further implicated inflammatory pathways and metabolism. We performed lipidomic analysis of HD and control brains and identified several lipid species that correlate with HD grade, including ceramides and very long chain fatty acids. Integration of lipidomics and bulk transcriptomics identified a consensus gene signature that correlates with HD grade and HD lipidomic abnormalities and implicated the unfolded protein response pathway. Because astrocytes are critical for brain lipid metabolism and play important roles in regulating inflammation, we analyzed our snRNAseq dataset with an emphasis on astrocyte pathology. We found two main astrocyte types that spanned multiple brain regions; these types correspond to protoplasmic astrocytes, and fibrous-like - CD44-positive, astrocytes. HD pathology was differentially associated with these cell types in a region-specific manner. One protoplasmic astrocyte cluster showed high expression of metallothionein genes, the depletion of this cluster positively correlated with the depletion of vulnerable medium spiny neurons in the caudate nucleus. We confirmed that metallothioneins were increased in cingulate HD astrocytes but were unchanged or even decreased in caudate astrocytes. We combined existing genome-wide association studies (GWAS) with a GWA study conducted on HD patients from the original Venezuelan cohort and identified a single-nucleotide polymorphism in the metallothionein gene locus associated with delayed age of onset. Functional studies found that metallothionein overexpressing astrocytes are better able to buffer glutamate and were neuroprotective of patient-derived directly reprogrammed HD MSNs as well as against rotenone-induced neuronal death in vitro. Finally, we found that metallothionein-overexpressing astrocytes increased the phagocytic activity of microglia in vitro and increased the expression of genes involved in fatty acid binding. Together, we identified an astrocytic phenotype that is regionally-enriched in less vulnerable brain regions that can be leveraged to protect neurons in HD.

Cell type-specific attenuation of brassinosteroid signaling precedes stomatal asymmetric cell division

In Arabidopsis thaliana, brassinosteroid (BR) signaling and stomatal development are connected through the SHAGGY/GSK3-like kinase BR INSENSITIVE2 (BIN2). BIN2 is a key negative regulator of BR signaling but it plays a dual role in stomatal development. BIN2 promotes or restricts stomatal asymmetric cell division (ACD) depending on its subcellular localization, which is regulated by the stomatal lineage-specific scaffold protein POLAR. BRs inactivate BIN2, but how they govern stomatal development remains unclear. Mapping the single-cell transcriptome of stomatal lineages after triggering BR signaling with either exogenous BRs or the specific BIN2 inhibitor, bikinin, revealed that the two modes of BR signaling activation generate spatiotemporally distinct transcriptional responses. We established that BIN2 is always sensitive to the inhibitor but, when in a complex with POLAR and its closest homolog POLAR-LIKE1, it becomes protected from BR-mediated inactivation. Subsequently, BR signaling in ACD precursors is attenuated, while it remains active in epidermal cells devoid of scaffolds and undergoing differentiation. Our study demonstrates how scaffold proteins contribute to cellular signal specificity of hormonal responses in plants.

Cell-connectivity-guided trajectory inference from single-cell data

MOTIVATION

Single-cell RNA-sequencing enables cell-level investigation of cell differentiation, which can be modelled using trajectory inference methods. While tremendous effort has been put into designing these methods, inferring accurate trajectories automatically remains difficult. Therefore, the standard approach involves testing different trajectory inference methods and picking the trajectory giving the most biologically sensible model. As the default parameters are often suboptimal, their tuning requires methodological expertise.

RESULTS

We introduce Totem, an open-source, easy-to-use R package designed to facilitate inference of tree-shaped trajectories from single-cell data. Totem generates a large number of clustering results, estimates their topologies as minimum spanning trees, and uses them to measure the connectivity of the cells. Besides automatic selection of an appropriate trajectory, cell connectivity enables to visually pinpoint branching points and milestones relevant to the trajectory. Furthermore, testing different trajectories with Totem is fast, easy, and does not require in-depth methodological knowledge.

AVAILABILITY AND IMPLEMENTATION

Totem is available as an R package at https://github.com/elolab/Totem.

Changes in subcutaneous white adipose tissue cellular composition and molecular programs underlie glucose intolerance in persons with HIV

Introduction

Subcutaneous adipose tissue (SAT) is a critical regulator of systemic metabolic homeostasis. Persons with HIV (PWH) have an increased risk of metabolic diseases and significant alterations in the SAT immune environment compared with the general population.

Methods

We generated a comprehensive single-cell multi-omic SAT atlas to characterize cellular compositional and transcriptional changes in 59 PWH across a spectrum of metabolic health.

Results

Glucose intolerance was associated with increased lipid-associated macrophages, CD4+ and CD8+ T effector memory cells, and decreased perivascular macrophages. We observed a coordinated intercellular regulatory program which enriched for genes related to inflammation and lipid-processing across multiple cell types as glucose intolerance increased. Increased CD4+ effector memory tissue-resident cells most strongly associated with altered expression of adipocyte genes critical for lipid metabolism and cellular regulation. Intercellular communication analysis demonstrated enhanced pro-inflammatory and pro-fibrotic signaling between immune cells and stromal cells in PWH with glucose intolerance compared with non-diabetic PWH. Lastly, while cell type-specific gene expression among PWH with diabetes was globally similar to HIV-negative individuals with diabetes, we observed substantially divergent intercellular communication pathways.

Discussion

These findings suggest a central role of tissue-resident immune cells in regulating SAT inflammation among PWH with metabolic disease, and underscore unique mechanisms that may converge to promote metabolic disease.

Dynamic peripheral blood microRNA expression landscape during the peri-implantation stage in women with successful pregnancy achieved by single frozen-thawed blastocyst transfer

STUDY QUESTION

What are the dynamic expression features of plasma microRNAs (miRNAs) during the peri-implantation period in women with successful pregnancy via single frozen-thawed blastocyst transfer?

SUMMARY ANSWER

There is a significant change in the plasma miRNA expression profile before and after blastocyst transfer, during the window of implantation.

WHAT IS KNOWN ALREADY

The expression of miRNAs in peripheral blood has indicative functions during the peri-implantation period. Nevertheless, the dynamic expression profile of circulating miRNAs during the peri-implantation stage in women with a successful pregnancy has not been studied.

STUDY DESIGN SIZE DURATION

Seventy-six women treated for infertility with a single frozen-thawed blastocyst transfer in a natural cycle were included in this study. Among them, 57 women had implantation success and a live birth, while 19 patients experienced implantation failure. Peripheral blood samples were collected at five different time points throughout the peri-implantation period, including D0 (ovulation day), D3, D5, D7, and D9 in this cycle of embryo transfer. The plasma miRNAs in women with blastocyst transfer were isolated, sequenced, and analyzed.

PARTICIPANTS/MATERIALS SETTING METHODS

Peripheral blood samples were collected in EDTA tubes and stored at -80°C until further use. miRNAs were isolated from blood, cDNA libraries were constructed, and the resulting sequences were mapped to the human genome. The plasma miRNAs were initially analyzed in a screening cohort (n = 34) with successful pregnancy. Trajectory analysis, including a global test and pairwise comparisons, was performed to detect dynamic differentially expressed (DE) miRNAs. Fuzzy c-means clustering was conducted for all dynamic DE miRNAs. The correlation between DE miRNAs and clinical characteristics of patients was investigated using a linear mixed model. Target genes of the miRNAs were predicted, and functional annotation analysis was performed. The expression of DE miRNAs was also identified in a validation set consisting of women with successful (n = 23) and unsuccessful (n = 19) pregnancies.

MAIN RESULTS AND THE ROLE OF CHANCE

Following small RNA sequencing, a total of 2656 miRNAs were determined as valid read values. After trajectory analysis, 26 DE miRNAs (false discovery rate < 0.05) were identified by the global test, while pairwise comparisons in addition identified 20 DE miRNAs. A total of seven distinct clusters representing different temporal patterns of miRNA expression were discovered. Nineteen DE miRNAs were further identified to be associated with at least one clinical trait. Endometrium thickness and progesterone level showed a correlation with multiple DE miRNAs (including two of the same miRNAs, hsa-miR-1-3p and hsa-miR-6741-3p). Moreover, the 19 DE miRNAs were predicted to have 403 gene targets, and there were 51 (12.7%) predicted genes likely involved in both decidualization and embryo implantation. Functional annotation for predicted targets of those clinically related DE miRNAs suggested the involvement of vascular endothelial growth factor and Wnt signaling pathways, as well as responses to hormones, immune responses, and cell adhesion-related signaling pathways during the peri-implantation stage.

LARGE SCALE DATA

The raw miRNA sequence data reported in this article have been deposited in the Genome Sequence Archive (GSA-Human: HRA005227) and are publicly accessible at https://ngdc.cncb.ac.cn/gsa-human/browse/HRA005227.

LIMITATIONS REASONS FOR CAUTION

Although the RNA sequencing results revealed the global dynamic changes of miRNA expression, further experiments examining the clinical significance of the identified DE miRNAs in embryo implantation outcome and the relevant regulatory mechanisms involved are warranted.

WIDER IMPLICATIONS OF THE FINDINGS

Understanding the dynamic landscape of the miRNA transcriptome could shed light on the physiological mechanisms involved from ovulation to the post-implantation stage, as well as identifying biomarkers that characterize stage-related biological process.

STUDY FUNDING/COMPETING INTERESTS

The study was funded by the Major clinical research project of Tangdu Hospital (2021LCYJ004) and the Discipline Platform Improvement Plan of Tangdu Hospital (2020XKPT003). The funders had no influence on the study design, data collection, and analysis, decision to publish, or preparation of the article. There are no conflicts of interest to declare.

Defining the molecular correlate of arteriolar hyalinosis in kidney disease progression by integration of single cell transcriptomic analysis and pathology scoring

Arteriolar hyalinosis in kidneys is an independent predictor of cardiovascular disease, the main cause of mortality in chronic kidney disease (CKD). The underlying molecular mechanisms of protein accumulation in the subendothelial space are not well understood. Using single cell transcriptomic data and whole slide images from kidney biopsies of patients with CKD and acute kidney injury in the Kidney Precision Medicine Project, the molecular signals associated with arteriolar hyalinosis were evaluated. Co-expression network analysis of the endothelial genes yielded three gene set modules as significantly associated with arteriolar hyalinosis. Pathway analysis of these modules showed enrichment of transforming growth factor beta / bone morphogenetic protein (TGFβ / BMP) and vascular endothelial growth factor (VEGF) signaling pathways in the endothelial cell signatures. Ligand-receptor analysis identified multiple integrins and cell adhesion receptors as over-expressed in arteriolar hyalinosis, suggesting a potential role of integrin-mediated TGFβ signaling. Further analysis of arteriolar hyalinosis associated endothelial module genes identified focal segmental glomerular sclerosis as an enriched term. On validation in gene expression profiles from the Nephrotic Syndrome Study Network cohort, one of the three modules was significantly associated with the composite endpoint (> 40% reduction in estimated glomerular filtration rate (eGFR) or kidney failure) independent of age, sex, race, and baseline eGFR, suggesting poor prognosis with elevated expression of genes in this module. Thus, integration of structural and single cell molecular features yielded biologically relevant gene sets, signaling pathways and ligand-receptor interactions, underlying arteriolar hyalinosis and putative targets for therapeutic intervention.

Erythropoietin re-wires cognition-associated transcriptional networks

Recombinant human erythropoietin (rhEPO) has potent procognitive effects, likely hematopoiesis-independent, but underlying mechanisms and physiological role of brain-expressed EPO remained obscure. Here, we provide transcriptional hippocampal profiling of male mice treated with rhEPO. Based on ~108,000 single nuclei, we unmask multiple pyramidal lineages with their comprehensive molecular signatures. By temporal profiling and gene regulatory analysis, we build developmental trajectory of CA1 pyramidal neurons derived from multiple predecessor lineages and elucidate gene regulatory networks underlying their fate determination. With EPO as 'tool', we discover populations of newly differentiating pyramidal neurons, overpopulating to ~200% upon rhEPO with upregulation of genes crucial for neurodifferentiation, dendrite growth, synaptogenesis, memory formation, and cognition. Using a Cre-based approach to visually distinguish pre-existing from newly formed pyramidal neurons for patch-clamp recordings, we learn that rhEPO treatment differentially affects excitatory and inhibitory inputs. Our findings provide mechanistic insight into how EPO modulates neuronal functions and networks.

Generative modeling of single-cell gene expression for dose-dependent chemical perturbations

Single-cell sequencing reveals the heterogeneity of cellular response to chemical perturbations. However, testing all relevant combinations of cell types, chemicals, and doses is a daunting task. A deep generative learning formalism called variational autoencoders (VAEs) has been effective in predicting single-cell gene expression perturbations for single doses. Here, we introduce single-cell variational inference of dose-response (scVIDR), a VAE-based model that predicts both single-dose and multiple-dose cellular responses better than existing models. We show that scVIDR can predict dose-dependent gene expression across mouse hepatocytes, human blood cells, and cancer cell lines. We biologically interpret the latent space of scVIDR using a regression model and use scVIDR to order individual cells based on their sensitivity to chemical perturbation by assigning each cell a "pseudo-dose" value. We envision that scVIDR can help reduce the need for repeated animal testing across tissues, chemicals, and doses.

A defined roadmap of radial glia and astrocyte differentiation from human pluripotent stem cells

Human gliogenesis remains poorly understood, and derivation of astrocytes from human pluripotent stem cells (hPSCs) is inefficient and cumbersome. Here, we report controlled glial differentiation from hPSCs that bypasses neurogenesis, which otherwise precedes astrogliogenesis during brain development and in vitro differentiation. hPSCs were first differentiated into radial glial cells (RGCs) resembling resident RGCs of the fetal telencephalon, and modulation of specific cell signaling pathways resulted in direct and stepwise induction of key astroglial markers (NFIA, NFIB, SOX9, CD44, S100B, glial fibrillary acidic protein [GFAP]). Transcriptomic and genome-wide epigenetic mapping and single-cell analysis confirmed RGC-to-astrocyte differentiation, obviating neurogenesis and the gliogenic switch. Detailed molecular and cellular characterization experiments uncovered new mechanisms and markers for human RGCs and astrocytes. In summary, establishment of a glia-exclusive neural lineage progression model serves as a unique serum-free platform of manufacturing large numbers of RGCs and astrocytes for neuroscience, disease modeling (e.g., Alexander disease), and regenerative medicine.

Quantifying common and distinct information in single-cell multimodal data with Tilted Canonical Correlation Analysis

Multimodal single-cell technologies profile multiple modalities for each cell simultaneously, enabling a more thorough characterization of cell populations. Existing dimension-reduction methods for multimodal data capture the "union of information," producing a lower-dimensional embedding that combines the information across modalities. While these tools are useful, we focus on a fundamentally different task of separating and quantifying the information among cells that is shared between the two modalities as well as unique to only one modality. Hence, we develop Tilted Canonical Correlation Analysis (Tilted-CCA), a method that decomposes a paired multimodal dataset into three lower-dimensional embeddings-one embedding captures the "intersection of information," representing the geometric relations among the cells that is common to both modalities, while the remaining two embeddings capture the "distinct information for a modality," representing the modality-specific geometric relations. We analyze single-cell multimodal datasets sequencing RNA along surface antibodies (i.e., CITE-seq) as well as RNA alongside chromatin accessibility (i.e., 10x) for blood cells and developing neurons via Tilted-CCA. These analyses show that Tilted-CCA enables meaningful visualization and quantification of the cross-modal information. Finally, Tilted-CCA's framework allows us to perform two specific downstream analyses. First, for single-cell datasets that simultaneously profile transcriptome and surface antibody markers, we show that Tilted-CCA helps design the target antibody panel to complement the transcriptome best. Second, for developmental single-cell datasets that simultaneously profile transcriptome and chromatin accessibility, we show that Tilted-CCA helps identify development-informative genes and distinguish between transient versus terminal cell types.

Exploring the optimization of autoencoder design for imputing single-cell RNA sequencing data

Autoencoders are the backbones of many imputation methods that aim to relieve the sparsity issue in single-cell RNA sequencing (scRNA-seq) data. The imputation performance of an autoencoder relies on both the neural network architecture and the hyperparameter choice. So far, literature in the single-cell field lacks a formal discussion on how to design the neural network and choose the hyperparameters. Here, we conducted an empirical study to answer this question. Our study used many real and simulated scRNA-seq datasets to examine the impacts of the neural network architecture, the activation function, and the regularization strategy on imputation accuracy and downstream analyses. Our results show that (i) deeper and narrower autoencoders generally lead to better imputation performance; (ii) the sigmoid and tanh activation functions consistently outperform other commonly used functions including ReLU; (iii) regularization improves the accuracy of imputation and downstream cell clustering and DE gene analyses. Notably, our results differ from common practices in the computer vision field regarding the activation function and the regularization strategy. Overall, our study offers practical guidance on how to optimize the autoencoder design for scRNA-seq data imputation.

Deciphering the Biology of Circulating Tumor Cells through Single-Cell RNA Sequencing: Implications for Precision Medicine in Cancer

Circulating tumor cells (CTCs) hold unique biological characteristics that directly involve them in hematogenous dissemination. Studying CTCs systematically is technically challenging due to their extreme rarity and heterogeneity and the lack of specific markers to specify metastasis-initiating CTCs. With cutting-edge technology, single-cell RNA sequencing (scRNA-seq) provides insights into the biology of metastatic processes driven by CTCs. Transcriptomics analysis of single CTCs can decipher tumor heterogeneity and phenotypic plasticity for exploring promising novel therapeutic targets. The integrated approach provides a perspective on the mechanisms underlying tumor development and interrogates CTCs interactions with other blood cell types, particularly those of the immune system. This review aims to comprehensively describe the current study on CTC transcriptomic analysis through scRNA-seq technology. We emphasize the workflow for scRNA-seq analysis of CTCs, including enrichment, single cell isolation, and bioinformatic tools applied for this purpose. Furthermore, we elucidated the translational knowledge from the transcriptomic profile of individual CTCs and the biology of cancer metastasis for developing effective therapeutics through targeting key pathways in CTCs.