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

Determining cellular lineage directed networks in hematopoiesis using single-cell transcriptomic data and volatility-constrained correlation

Single-cell transcriptome sequencing (scRNA-seq) has revolutionized our understanding of cellular processes by enabling the analysis of expression profiles at an individual cell level. This technology has shown promise in uncovering new cell types, gene functions, cell differentiation, and trajectory inference through the study of various biological processes, such as hematopoiesis. Recent scRNA-seq analysis of mouse bone marrow cells has provided a network model of hematopoietic lineage. However, all data analyses have predicted undirected network maps for the associated cell trajectories. Moreover, the debate regarding the origin of basophil cells still persists. In this work, we apply the Volatility Constrained (VC) correlation method to predict not only the network structure but also the causality or directionality between the cell types present in the hematopoietic process. Our findings suggest a dual origin of basophils, from both granulocyte/macrophage and erythrocyte progenitors, the latter being a trajectory less explored in previous research. The proposed approach and predictions may assist in developing a complete hematopoietic process map, impacting our understanding of hematopoiesis and providing a robust directional network framework for further biomedical research.

Tumour vasculature at single-cell resolution

Tumours can obtain nutrients and oxygen required to progress and metastasize through the blood supply1. Inducing angiogenesis involves the sprouting of established vessel beds and their maturation into an organized network2,3. Here we generate a comprehensive atlas of tumour vasculature at single-cell resolution, encompassing approximately 200,000 cells from 372 donors representing 31 cancer types. Trajectory inference suggested that tumour angiogenesis was initiated from venous endothelial cells and extended towards arterial endothelial cells. As neovascularization elongates (through angiogenic stages SI, SII and SIII), APLN+ tip cells at the SI stage (APLN+ TipSI) advanced to TipSIII cells with increased Notch signalling. Meanwhile, stalk cells, following tip cells, transitioned from high chemokine expression to elevated TEK (also known as Tie2) expression. Moreover, APLN+ TipSI cells not only were associated with disease progression and poor prognosis but also hold promise for predicting response to anti-VEGF therapy. Lymphatic endothelial cells demonstrated two distinct differentiation lineages: one responsible for lymphangiogenesis and the other involved in antigen presentation. In pericytes, endoplasmic reticulum stress was associated with the proangiogenic BASP1+ matrix-producing pericytes. Furthermore, intercellular communication analysis showed that neovascular endothelial cells could shape an immunosuppressive microenvironment conducive to angiogenesis. This study depicts the complexity of tumour vasculature and has potential clinical significance for anti-angiogenic therapy.

Mapping spatially resolved transcriptomes in human and mouse pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with poor prognosis and limited treatment options. Efforts to identify effective treatments are thwarted by limited understanding of IPF pathogenesis and poor translatability of available preclinical models. Here we generated spatially resolved transcriptome maps of human IPF (n = 4) and bleomycin-induced mouse pulmonary fibrosis (n = 6) to address these limitations. We uncovered distinct fibrotic niches in the IPF lung, characterized by aberrant alveolar epithelial cells in a microenvironment dominated by transforming growth factor beta signaling alongside predicted regulators, such as TP53 and APOE. We also identified a clear divergence between the arrested alveolar regeneration in the IPF fibrotic niches and the active tissue repair in the acutely fibrotic mouse lung. Our study offers in-depth insights into the IPF transcriptional landscape and proposes alveolar regeneration as a promising therapeutic strategy for IPF.

scHD4E: Novel ensemble learning-based differential expression analysis method for single-cell RNA-sequencing data

Differential expression (DE) analysis between cell types for scRNA-seq data by capturing its complicated features is crucial. Recently, different methods have been developed for targeting the scRNA-seq data analysis based on different modeling frameworks, assumptions, strategies and test statistic in considering various data features. The scDEA is an ensemble learning-based DE analysis method developed recently, yielding p-values using Lancaster's combination, generated by 12 individual DE analysis methods, and producing more accurate and stable results than individual methods. The objective of our study is to propose a new ensemble learning-based DE analysis method, scHD4E, using top performers in only 4 separate methods. The top performer 4 methods have been selected through an evaluation process using six real scRNA-seq data sets. We conducted comprehensive experiments for five experimental data sets to evaluate our proposed method based on the sample size effects, batch effects, type I error control, gene ontology enrichment analysis, runtime, identified matched DE genes, and semantic similarity measurement between methods. We also perform similar analyses (except the last 3 terms) and compute performance measures like accuracy, F1 score, Mathew's correlation coefficient etc. for a simulated data set. The results show that scHD4E is performs better than all the individual and scDEA methods in all the above perspectives. We expect that scHD4E will serve the modern data scientists for detecting the DEGs in scRNA-seq data analysis. To implement our proposed method, a Github R package scHD4E and its shiny application has been developed, and available in the following links: https://github.com/bbiswas1989/scHD4E and https://github.com/bbiswas1989/scHD4E-Shiny.

A group 3 medulloblastoma stem cell program is maintained by OTX2-mediated alternative splicing

OTX2 is a transcription factor and known driver in medulloblastoma (MB), where it is amplified in a subset of tumours and overexpressed in most cases of group 3 and group 4 MB. Here we demonstrate a noncanonical role for OTX2 in group 3 MB alternative splicing. OTX2 associates with the large assembly of splicing regulators complex through protein-protein interactions and regulates a stem cell splicing program. OTX2 can directly or indirectly bind RNA and this may be partially independent of its DNA regulatory functions. OTX2 controls a pro-tumorigenic splicing program that is mirrored in human cerebellar rhombic lip origins. Among the OTX2-regulated differentially spliced genes, PPHLN1 is expressed in the most primitive rhombic lip stem cells, and targeting PPHLN1 splicing reduces tumour growth and enhances survival in vivo. These findings identify OTX2-mediated alternative splicing as a major determinant of cell fate decisions that drive group 3 MB progression.

Oncostatin M-driven macrophage-fibroblast circuits as a drug target in autoimmune arthritis

BACKGROUND

Recent single-cell RNA sequencing (scRNA-seq) analysis revealed the functional heterogeneity and pathogenic cell subsets in immune cells, synovial fibroblasts and bone cells in rheumatoid arthritis (RA). JAK inhibitors which ameliorate joint inflammation and bone destruction in RA, suppress the activation of various types of cells in vitro. However, the key cellular and molecular mechanisms underlying the potent clinical effects of JAK inhibitors on RA remain to be determined. Our aim is to identify a therapeutic target for JAK inhibitors in vivo.

METHODS

We performed scRNA-seq analysis of the synovium of collagen-induced arthritis (CIA) mice treated with or without a JAK inhibitor, followed by a computational analysis to identify the drug target cells and signaling pathways. We utilized integrated human RA scRNA-seq datasets and genetically modified mice administered with the JAK inhibitor for the confirmation of our findings.

RESULTS

scRNA-seq analysis revealed that oncostatin M (OSM) driven macrophage-fibroblast interaction is highly activated under arthritic conditions. OSM derived from macrophages, acts on OSM receptor (OSMR)-expressing synovial fibroblasts, activating both inflammatory and tissue-destructive subsets. Inflammatory synovial fibroblasts stimulate macrophages, mainly through IL-6, to exacerbate inflammation. Tissue-destructive synovial fibroblasts promote osteoclast differentiation by producing RANKL to accelerate bone destruction. scRNA-seq analysis also revealed that OSM-signaling in synovial fibroblasts is the main signaling pathway targeted by JAK inhibitors in vivo. Mice specifically lacking OSMR in synovial fibroblasts (Osmr∆Fibro) displayed ameliorated inflammation and joint destruction in arthritis. The JAK inhibitor was effective on the arthritis of the control mice while it had no effect on the arthritis of Osmr∆Fibro mice.

CONCLUSIONS

OSM functions as one of the key cytokines mediating pathogenic macrophage-fibroblast interaction. OSM-signaling in synovial fibroblasts is one of the main signaling pathways targeted by JAK inhibitors in vivo. The critical role of fibroblast-OSM signaling in autoimmune arthritis was shown by a combination of mice specifically deficient for OSMR in synovial fibroblasts and administration of the JAK inhibitor. Thus, the OSM-driven synovial macrophage-fibroblast circuit is proven to be a key driver of autoimmune arthritis, serving as a crucial drug target in vivo.

Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE

Single-cell RNA-sequencing enables testing for differential expression (DE) between conditions at a cell type level. While powerful, one of the limitations of such approaches is that the sensitivity of DE testing is dictated by the sensitivity of clustering, which is often suboptimal. To overcome this, we present miloDE-a cluster-free framework for DE testing (available as an open-source R package). We illustrate the performance of miloDE on both simulated and real data. Using miloDE, we identify a transient hemogenic endothelia-like state in mouse embryos lacking Tal1 and detect distinct programs during macrophage activation in idiopathic pulmonary fibrosis.

Single-cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers

BACKGROUND

The encoding of cell intrinsic drug resistance states in breast cancer reflects the contributions of genomic and non-genomic variations and requires accurate estimation of clonal fitness from co-measurement of transcriptomic and genomic data. Somatic copy number (CN) variation is the dominant mutational mechanism leading to transcriptional variation and notably contributes to platinum chemotherapy resistance cell states. Here, we deploy time series measurements of triple negative breast cancer (TNBC) single-cell transcriptomes, along with co-measured single-cell CN fitness, identifying genomic and transcriptomic mechanisms in drug-associated transcriptional cell states.

RESULTS

We present scRNA-seq data (53,641 filtered cells) from serial passaging TNBC patient-derived xenograft (PDX) experiments spanning 2.5 years, matched with genomic single-cell CN data from the same samples. Our findings reveal distinct clonal responses within TNBC tumors exposed to platinum. Clones with high drug fitness undergo clonal sweeps and show subtle transcriptional reversion, while those with weak fitness exhibit dynamic transcription upon drug withdrawal. Pathway analysis highlights convergence on epithelial-mesenchymal transition and cytokine signaling, associated with resistance. Furthermore, pseudotime analysis demonstrates hysteresis in transcriptional reversion, indicating generation of new intermediate transcriptional states upon platinum exposure.

CONCLUSIONS

Within a polyclonal tumor, clones with strong genotype-associated fitness under platinum remained fixed, minimizing transcriptional reversion upon drug withdrawal. Conversely, clones with weaker fitness display non-genomic transcriptional plasticity. This suggests CN-associated and CN-independent transcriptional states could both contribute to platinum resistance. The dominance of genomic or non-genomic mechanisms within polyclonal tumors has implications for drug sensitivity, restoration, and re-treatment strategies.

Genomic and single-cell analyses reveal genetic signatures of swimming pattern and diapause strategy in jellyfish

Jellyfish exhibit innovative swimming patterns that contribute to exploring the origins of animal locomotion. However, the genetic and cellular basis of these patterns remains unclear. Herein, we generated chromosome-level genome assemblies of two jellyfish species, Turritopsis rubra and Aurelia coerulea, which exhibit straight and free-swimming patterns, respectively. We observe positive selection of numerous genes involved in statolith formation, hair cell ciliogenesis, ciliary motility, and motor neuron function. The lineage-specific absence of otolith morphogenesis- and ciliary movement-related genes in T. rubra may be associated with homeostatic structural statocyst loss and straight swimming pattern. Notably, single-cell transcriptomic analyses covering key developmental stages reveal the enrichment of diapause-related genes in the cyst during reverse development, suggesting that the sustained diapause state favours the development of new polyps under favourable conditions. This study highlights the complex relationship between genetics, locomotion patterns and survival strategies in jellyfish, thereby providing valuable insights into the evolutionary lineages of movement and adaptation in the animal kingdom.

Single-cell total-RNA profiling unveils regulatory hubs of transcription factors

Recent development of RNA velocity uses master equations to establish the kinetics of the life cycle of RNAs from unspliced RNA to spliced RNA (i.e., mature RNA) to degradation. To feed this kinetic analysis, simultaneous measurement of unspliced RNA and spliced RNA in single cells is greatly desired. However, the majority of single-cell RNA-seq chemistry primarily captures mature RNA species to measure gene expressions. Here, we develop a one-step total-RNA chemistry-based single-cell RNA-seq method: snapTotal-seq. We benchmark this method with multiple single-cell RNA-seq assays in their performance in kinetic analysis of cell cycle by RNA velocity. Next, with LASSO regression between transcription factors, we identify the critical regulatory hubs mediating the cell cycle dynamics. We also apply snapTotal-seq to profile the oncogene-induced senescence and identify the key regulatory hubs governing the entry of senescence. Furthermore, from the comparative analysis of unspliced RNA and spliced RNA, we identify a significant portion of genes whose expression changes occur in spliced RNA but not to the same degree in unspliced RNA, indicating these gene expression changes are mainly controlled by post-transcriptional regulation. Overall, we demonstrate that snapTotal-seq can provide enriched information about gene regulation, especially during the transition between cell states.

Early human fetal lung atlas reveals the temporal dynamics of epithelial cell plasticity

Studying human fetal lungs can inform how developmental defects and disease states alter the function of the lungs. Here, we sequenced >150,000 single cells from 19 healthy human pseudoglandular fetal lung tissues ranging between gestational weeks 10-19. We capture dynamic developmental trajectories from progenitor cells that express abundant levels of the cystic fibrosis conductance transmembrane regulator (CFTR). These cells give rise to multiple specialized epithelial cell types. Combined with spatial transcriptomics, we show temporal regulation of key signalling pathways that may drive the temporal and spatial emergence of specialized epithelial cells including ciliated and pulmonary neuroendocrine cells. Finally, we show that human pluripotent stem cell-derived fetal lung models contain CFTR-expressing progenitor cells that capture similar lineage developmental trajectories as identified in the native tissue. Overall, this study provides a comprehensive single-cell atlas of the developing human lung, outlining the temporal and spatial complexities of cell lineage development and benchmarks fetal lung cultures from human pluripotent stem cell differentiations to similar developmental window.

Endogenous FGFs drive ERK-dependent cell fate patterning in 2D human gastruloids

The role of FGF is the least understood of the morphogens driving mammalian gastrulation. Here we investigated the function of FGF in a stem cell model for human gastrulation known as a 2D gastruloid. We found a ring of FGF-dependent ERK activity that closely follows the emergence of primitive streak (PS)-like cells but expands further inward. We showed that this ERK activity pattern is required for PS-like differentiation and that loss of PS-like cells upon FGF receptor inhibition can be rescued by directly activating ERK. We further demonstrated that the ERK-ring depends on localized activation of basally localized FGF receptors (FGFR) by endogenous FGF gradients. We confirm and extend previous studies in analyzing expression of FGF pathway components, showing the main receptor to be FGFR1 and the key ligands FGF2/4/17, similar to the human and monkey embryo but different from the mouse. In situ hybridization and scRNA-seq revealed that FGF4 and FGF17 expression colocalize with the PS marker TBXT but only FGF17 is maintained in nascent mesoderm and endoderm. FGF4 and FGF17 reduction both reduced ERK activity and differentiation to PS-like cells and their derivatives, indicating overlapping function. Thus, we have identified a previously unknown role for FGF-dependent ERK signaling in 2D gastruloids and possibly the human embryo, driven by a mechanism where FGF4 and FGF17 signal through basally localized FGFR1 to induce PS-like cells.

scMaSigPro: Differential Expression Analysis along Single-Cell Trajectories

MOTIVATION

Understanding the dynamics of gene expression across different cellular states is crucial for discerning the mechanisms underneath cellular differentiation. Genes that exhibit variation in mean expression as a function of Pseudotime and between branching trajectories are expected to govern cell fate decisions. We introduce scMaSigPro, a method for the identification of differential gene expression patterns along Pseudotime and branching paths simultaneously.

RESULTS

We assessed the performance of scMaSigPro using synthetic and public datasets. Our evaluation shows that scMaSigPro outperforms existing methods in controlling the False Positive Rate and is computationally efficient.

AVAILABILITY AND IMPLEMENTATION

scMaSigPro is available as a free R package (version 4.0 or higher) under the GPL(≥2) license on GitHub at 'github.com/BioBam/scMaSigPro' and archived with version 0.03 on Zenodo at 'zenodo.org/records/12568922'.

Stage-specific GATA3 induction promotes ILC2 development after lineage commitment

Group 2 innate lymphoid cells (ILC2s) are a subset of innate lymphocytes that produce type 2 cytokines, including IL-4, IL-5, and IL-13. GATA3 is a critical transcription factor for ILC2 development at multiple stages. However, when and how GATA3 is induced to the levels required for ILC2 development remains unclear. Herein, we identify ILC2-specific GATA3-related tandem super-enhancers (G3SE) that induce high GATA3 in ILC2-committed precursors. G3SE-deficient mice exhibit ILC2 deficiency in the bone marrow, lung, liver, and small intestine with minimal impact on other ILC lineages or Th2 cells. Single-cell RNA-sequencing and subsequent flow cytometry analysis show that GATA3 induction mechanism, which is required for entering the ILC2 stage, is lost in IL-17RB+PD-1- late ILC2-committed precursor stage in G3SE-deficient mice. Cnot6l, part of the CCR4-NOT deadenylase complex, is a possible GATA3 target during ILC2 development. Our findings implicate a stage-specific regulatory mechanism for GATA3 expression during ILC2 development.

Senescence Defines a Distinct Subset of Myofibroblasts That Orchestrates Immunosuppression in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) therapeutic resistance is largely attributed to a unique tumor microenvironment embedded with an abundance of cancer-associated fibroblasts (CAF). Distinct CAF populations were recently identified, but the phenotypic drivers and specific impact of CAF heterogeneity remain unclear. In this study, we identify a subpopulation of senescent myofibroblastic CAFs (SenCAF) in mouse and human PDAC. These SenCAFs are a phenotypically distinct subset of myofibroblastic CAFs that localize near tumor ducts and accumulate with PDAC progression. To assess the impact of endogenous SenCAFs in PDAC, we used an LSL-KRASG12D;p53flox;p48-CRE;INK-ATTAC (KPPC-IA) mouse model of spontaneous PDAC with inducible senescent cell depletion. Depletion of senescent stromal cells in genetic and pharmacologic PDAC models relieved immune suppression by macrophages, delayed tumor progression, and increased responsiveness to chemotherapy. Collectively, our findings demonstrate that SenCAFs promote PDAC progression and immune cell dysfunction. Significance: CAF heterogeneity in PDAC remains poorly understood. In this study, we identify a novel subpopulation of senescent CAFs that promotes PDAC progression and immunosuppression. Targeting CAF senescence in combination therapies could increase tumor vulnerability to chemo or immunotherapy. See related article by Ye et al., p. 1302.

Aging atlas reveals cell-type-specific effects of pro-longevity strategies

Organismal aging involves functional declines in both somatic and reproductive tissues. Multiple strategies have been discovered to extend lifespan across species. However, how age-related molecular changes differ among various tissues and how those lifespan-extending strategies slow tissue aging in distinct manners remain unclear. Here we generated the transcriptomic Cell Atlas of Worm Aging (CAWA, http://mengwanglab.org/atlas ) of wild-type and long-lived strains. We discovered cell-specific, age-related molecular and functional signatures across all somatic and germ cell types. We developed transcriptomic aging clocks for different tissues and quantitatively determined how three different pro-longevity strategies slow tissue aging distinctively. Furthermore, through genome-wide profiling of alternative polyadenylation (APA) events in different tissues, we discovered cell-type-specific APA changes during aging and revealed how these changes are differentially affected by the pro-longevity strategies. Together, this study offers fundamental molecular insights into both somatic and reproductive aging and provides a valuable resource for in-depth understanding of the diversity of pro-longevity mechanisms.

Molecular maps of synovial cells in inflammatory arthritis using an optimized synovial tissue dissociation protocol

In this study, we optimized the dissociation of synovial tissue biopsies for single-cell omics studies and created a single-cell atlas of human synovium in inflammatory arthritis. The optimized protocol allowed consistent isolation of highly viable cells from tiny fresh synovial biopsies, minimizing the synovial biopsy drop-out rate. The synovium scRNA-seq atlas contained over 100,000 unsorted synovial cells from 25 synovial tissues affected by inflammatory arthritis, including 16 structural, 11 lymphoid, and 15 myeloid cell clusters. This synovial cell map expanded the diversity of synovial cell types/states, detected synovial neutrophils, and broadened synovial endothelial cell classification. We revealed tissue-resident macrophage subsets with proposed matrix-sensing (FOLR2+COLEC12high) and iron-recycling (LYVE1+SLC40A1+) activities and identified fibroblast subsets with proposed functions in cartilage breakdown (SOD2highSAA1+SAA2+SDC4+) and extracellular matrix remodeling (SERPINE1+COL5A3+LOXL2+). Our study offers an efficient synovium dissociation method and a reference scRNA-seq resource, that advances the current understanding of synovial cell heterogeneity in inflammatory arthritis.

Genome-Wide CRISPR Screen Identifies an NF2-Adherens Junction Mechanistic Dependency for Cardiac Lineage

BACKGROUND

Cardiomyocyte differentiation involves a stepwise clearance of repressors and fate-restricting regulators through the modulation of BMP (bone morphogenic protein)/Wnt-signaling pathways. However, the mechanisms and how regulatory roadblocks are removed with specific developmental signaling pathways remain unclear.

METHODS

We conducted a genome-wide CRISPR screen to uncover essential regulators of cardiomyocyte specification in human embryonic stem cells using a myosin heavy chain 6 (MYH6)-GFP (green fluorescence protein) reporter system. After an independent secondary single guide ribonucleic acid validation of 25 candidates, we identified NF2 (neurofibromin 2), a moesin-ezrin-radixin like (MERLIN) tumor suppressor, as an upstream driver of early cardiomyocyte lineage specification. Independent monoclonal NF2 knockouts were generated using CRISPR-Cas9, and cell states were inferred through bulk RNA sequencing and protein expression analysis across differentiation time points. Terminal lineage differentiation was assessed by using an in vitro 2-dimensional-micropatterned gastruloid model, trilineage differentiation, and cardiomyocyte differentiation. Protein interaction and post-translation modification of NF2 with its interacting partners were assessed using site-directed mutagenesis, coimmunoprecipitation, and proximity ligation assays.

RESULTS

Transcriptional regulation and trajectory inference from NF2-null cells reveal the loss of cardiomyocyte identity and the acquisition of nonmesodermal identity. Sustained elevation of early mesoderm lineage repressor SOX2 and upregulation of late anticardiac regulators CDX2 and MSX1 in NF2 knockout cells reflect a necessary role for NF2 in removing regulatory roadblocks. Furthermore, we found that NF2 and AMOT (angiomotin) cooperatively bind to YAP (yes-associated protein) during mesendoderm formation, thereby preventing YAP activation, independent of canonical MST (mammalian sterile 20-like serine-threonine protein kinase)-LATS (large tumor suppressor serine-threonine protein kinase) signaling. Mechanistically, cardiomyocyte lineage identity was rescued by wild-type and NF2 serine-518 phosphomutants, but not NF2 FERM (ezrin-radixin-meosin homology protein) domain blue-box mutants, demonstrating that the critical FERM domain-dependent formation of the AMOT-NF2-YAP scaffold complex at the adherens junction is required for early cardiomyocyte lineage differentiation.

CONCLUSIONS

These results provide mechanistic insight into the essential role of NF2 during early epithelial-mesenchymal transition by sequestering the repressive effect of YAP and relieving regulatory roadblocks en route to cardiomyocytes.

LMNA -Related Dilated Cardiomyopathy: Single-Cell Transcriptomics during Patient-derived iPSC Differentiation Support Cell type and Lineage-specific Dysregulation of Gene Expression and Development for Cardiomyocytes and Epicardium-Derived Cells with Lamin A/C Haploinsufficiency

LMNA -Related Dilated Cardiomyopathy (DCM) is an autosomal-dominant genetic condition with cardiomyocyte and conduction system dysfunction often resulting in heart failure or sudden death. The condition is caused by mutation in the Lamin A/C ( LMNA ) gene encoding Type-A nuclear lamin proteins involved in nuclear integrity, epigenetic regulation of gene expression, and differentiation. Molecular mechanisms of disease are not completely understood, and there are no definitive treatments to reverse progression or prevent mortality. We investigated possible mechanisms of LMNA -Related DCM using induced pluripotent stem cells derived from a family with a heterozygous LMNA c.357-2A>G splice-site mutation. We differentiated one LMNA mutant iPSC line derived from an affected female (Patient) and two non-mutant iPSC lines derived from her unaffected sister (Control) and conducted single-cell RNA sequencing for 12 samples (4 Patient and 8 Control) across seven time points: Day 0, 2, 4, 9, 16, 19, and 30. Our bioinformatics workflow identified 125,554 cells in raw data and 110,521 (88%) high-quality cells in sequentially processed data. Unsupervised clustering, cell annotation, and trajectory inference found complex heterogeneity: ten main cell types; many possible subtypes; and lineage bifurcation for Cardiac Progenitors to Cardiomyocytes (CM) and Epicardium-Derived Cells (EPDC). Data integration and comparative analyses of Patient and Control cells found cell type and lineage differentially expressed genes (DEG) with enrichment to support pathway dysregulation. Top DEG and enriched pathways included: 10 ZNF genes and RNA polymerase II transcription in Pluripotent cells (PP); BMP4 and TGF Beta/BMP signaling, sarcomere gene subsets and cardiogenesis, CDH2 and EMT in CM; LMNA and epigenetic regulation and DDIT4 and mTORC1 signaling in EPDC. Top DEG also included: XIST and other X-linked genes, six imprinted genes: SNRPN , PWAR6 , NDN , PEG10 , MEG3 , MEG8 , and enriched gene sets in metabolism, proliferation, and homeostasis. We confirmed Lamin A/C haploinsufficiency by allelic expression and Western blot. Our complex Patient-derived iPSC model for Lamin A/C haploinsufficiency in PP, CM, and EPDC provided support for dysregulation of genes and pathways, many previously associated with Lamin A/C defects, such as epigenetic gene expression, signaling, and differentiation. Our findings support disruption of epigenomic developmental programs as proposed in other LMNA disease models. We recognized other factors influencing epigenetics and differentiation; thus, our approach needs improvement to further investigate this mechanism in an iPSC-derived model.

CHIT1 at diagnosis predicts faster disability progression and reflects early microglial activation in multiple sclerosis

Multiple sclerosis (MS) is characterized by heterogeneity in disease course and prediction of long-term outcome remains a major challenge. Here, we investigate five myeloid markers - CHIT1, CHI3L1, sTREM2, GPNMB and CCL18 - in the cerebrospinal fluid (CSF) at diagnostic lumbar puncture in a longitudinal cohort of 192 MS patients. Through mixed-effects and machine learning models, we show that CHIT1 is a robust predictor for faster disability progression. Integrative analysis of 11 CSF and 26 central nervous system (CNS) parenchyma single-cell/nucleus RNA sequencing samples reveals CHIT1 to be predominantly expressed by microglia located in active MS lesions and enriched for lipid metabolism pathways. Furthermore, we find CHIT1 expression to accompany the transition from a homeostatic towards a more activated, MS-associated cell state in microglia. Neuropathological evaluation in post-mortem tissue from 12 MS patients confirms CHIT1 production by lipid-laden phagocytes in actively demyelinating lesions, already in early disease stages. Altogether, we provide a rationale for CHIT1 as an early biomarker for faster disability progression in MS.

A temporal developmental map separates human NK cells from noncytotoxic ILCs through clonal and single-cell analysis

ABSTRACT

Natural killer (NK) cells represent the cytotoxic member within the innate lymphoid cell (ILC) family that are important against viral infections and cancer. Although the NK cell emergence from hematopoietic stem and progenitor cells through multiple intermediate stages and the underlying regulatory gene network has been extensively studied in mice, this process is not well characterized in humans. Here, using a temporal in vitro model to reconstruct the developmental trajectory of NK lineage, we identified an ILC-restricted oligopotent stage 3a CD34-CD117+CD161+CD45RA+CD56- progenitor population, that exclusively gave rise to CD56-expressing ILCs in vitro. We also further investigated a previously nonappreciated heterogeneity within the CD56+CD94-NKp44+ subset, phenotypically equivalent to stage 3b population containing both group-1 ILC and RORγt+ ILC3 cells, that could be further separated based on their differential expression of DNAM-1 and CD161 receptors. We confirmed that DNAM-1hi S3b and CD161hiCD117hi ILC3 populations distinctively differed in their expression of effector molecules, cytokine secretion, and cytotoxic activity. Furthermore, analysis of lineage output using DNA-barcode tracing across these stages supported a close developmental relationship between S3b-NK and S4-NK (CD56+CD94+) cells, whereas distant to the ILC3 subset. Cross-referencing gene signatures of culture-derived NK cells and other noncytotoxic ILCs with publicly available data sets validated that these in vitro stages highly resemble transcriptional profiles of respective in vivo ILC counterparts. Finally, by integrating RNA velocity and gene network analysis through single-cell regulatory network inference and clustering we unravel a network of coordinated and highly dynamic regulons driving the cytotoxic NK cell program, as a guide map for future studies on NK cell regulation.

Single-cell transcriptomic atlas reveals increased regeneration in diseased human inner ear balance organs

Mammalian inner ear hair cell loss leads to permanent hearing and balance dysfunction. In contrast to the cochlea, vestibular hair cells of the murine utricle have some regenerative capacity. Whether human utricular hair cells regenerate in vivo remains unknown. Here we procured live, mature utricles from organ donors and vestibular schwannoma patients, and present a validated single-cell transcriptomic atlas at unprecedented resolution. We describe markers of 13 sensory and non-sensory cell types, with partial overlap and correlation between transcriptomes of human and mouse hair cells and supporting cells. We further uncover transcriptomes unique to hair cell precursors, which are unexpectedly 14-fold more abundant in vestibular schwannoma utricles, demonstrating the existence of ongoing regeneration in humans. Lastly, supporting cell-to-hair cell trajectory analysis revealed 5 distinct patterns of dynamic gene expression and associated pathways, including Wnt and IGF-1 signaling. Our dataset constitutes a foundational resource, accessible via a web-based interface, serving to advance knowledge of the normal and diseased human inner ear.

Alternative platelet differentiation pathways initiated by nonhierarchically related hematopoietic stem cells

Rare multipotent stem cells replenish millions of blood cells per second through a time-consuming process, passing through multiple stages of increasingly lineage-restricted progenitors. Although insults to the blood-forming system highlight the need for more rapid blood replenishment from stem cells, established models of hematopoiesis implicate only one mandatory differentiation pathway for each blood cell lineage. Here, we establish a nonhierarchical relationship between distinct stem cells that replenish all blood cell lineages and stem cells that replenish almost exclusively platelets, a lineage essential for hemostasis and with important roles in both the innate and adaptive immune systems. These distinct stem cells use cellularly, molecularly and functionally separate pathways for the replenishment of molecularly distinct megakaryocyte-restricted progenitors: a slower steady-state multipotent pathway and a fast-track emergency-activated platelet-restricted pathway. These findings provide a framework for enhancing platelet replenishment in settings in which slow recovery of platelets remains a major clinical challenge.

Human Umbilical Cord Mesenchymal Stromal Cell-Derived Extracellular Vesicles Induce Fetal Wound Healing Features Revealed by Single-Cell RNA Sequencing

The potential of human umbilical cord mesenchymal stromal cell-derived extracellular vesicles (hucMSC-EVs) in wound healing is promising, yet a comprehensive understanding of how fibroblasts and keratinocytes respond to this treatment remains limited. This study utilizes single-cell RNA sequencing (scRNA-seq) to investigate the impact of hucMSC-EVs on the cutaneous wound microenvironment in mice. Through rigorous single-cell analyses, we unveil the emergence of hucMSC-EV-induced hematopoietic fibroblasts and MMP13+ fibroblasts. Notably, MMP13+ fibroblasts exhibit fetal-like expressions of MMP13, MMP9, and HAS1, accompanied by heightened migrasome activity. Activation of MMP13+ fibroblasts is orchestrated by a distinctive PIEZO1-calcium-HIF1α-VEGF-MMP13 pathway, validated through murine models and dermal fibroblast assays. Organotypic culture assays further affirm that these activated fibroblasts induce keratinocyte migration via MMP13-LRP1 interactions. This study significantly contributes to our understanding of fibroblast heterogeneities as well as intercellular interactions in wound healing and identifies hucMSC-EV-induced hematopoietic fibroblasts as potential targets for reprogramming. The therapeutic targets presented by these fibroblasts offer exciting prospects for advancing wound healing strategies.

Single-Cell Sequencing Technology in Ruminant Livestock: Challenges and Opportunities

Advancements in single-cell sequencing have transformed the genomics field by allowing researchers to delve into the intricate cellular heterogeneity within tissues at greater resolution. While single-cell omics are more widely applied in model organisms and humans, their use in livestock species is just beginning. Studies in cattle, sheep, and goats have already leveraged single-cell and single-nuclei RNA-seq as well as single-cell and single-nuclei ATAC-seq to delineate cellular diversity in tissues, track changes in cell populations and gene expression over developmental stages, and characterize immune cell populations important for disease resistance and resilience. Although challenges exist for the use of this technology in ruminant livestock, such as the precise annotation of unique cell populations and spatial resolution of cells within a tissue, there is vast potential to enhance our understanding of the cellular and molecular mechanisms underpinning traits essential for healthy and productive livestock. This review intends to highlight the insights gained from published single-cell omics studies in cattle, sheep, and goats, particularly those with publicly accessible data. Further, this manuscript will discuss the challenges and opportunities of this technology in ruminant livestock and how it may contribute to enhanced profitability and sustainability of animal agriculture in the future.

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

Cell type-specific network analysis in Diversity Outbred mice identifies genes potentially responsible for human bone mineral density GWAS associations

Genome-wide association studies (GWASs) have identified many sources of genetic variation associated with bone mineral density (BMD), a clinical predictor of fracture risk and osteoporosis. Aside from the identification of causal genes, other difficult challenges to informing GWAS include characterizing the roles of predicted causal genes in disease and providing additional functional context, such as the cell type predictions or biological pathways in which causal genes operate. Leveraging single-cell transcriptomics (scRNA-seq) can assist in informing BMD GWAS by linking disease-associated variants to genes and providing a cell type context for which these causal genes drive disease. Here, we use large-scale scRNA-seq data from bone marrow-derived stromal cells cultured under osteogenic conditions (BMSC-OBs) from Diversity Outbred (DO) mice to generate cell type-specific networks and contextualize BMD GWAS-implicated genes. Using trajectories inferred from the scRNA-seq data, we identify networks enriched with genes that exhibit the most dynamic changes in expression across trajectories. We discover 21 network driver genes, which are likely to be causal for human BMD GWAS associations that colocalize with expression/splicing quantitative trait loci (eQTL/sQTL). These driver genes, including Fgfrl1 and Tpx2, along with their associated networks, are predicted to be novel regulators of BMD via their roles in the differentiation of mesenchymal lineage cells. In this work, we showcase the use of single-cell transcriptomics from mouse bone-relevant cells to inform human BMD GWAS and prioritize genetic targets with potential causal roles in the development of osteoporosis.

Clinical drug screening reveals clofazimine potentiates the efficacy while reducing the toxicity of anti-PD-1 and CTLA-4 immunotherapy

Emerging as the most potent and durable combinational immunotherapy, dual anti-PD-1 and CTLA-4 immune checkpoint blockade (ICB) therapy notoriously increases grade 3-5 immune-related adverse events (irAEs) in patients. Accordingly, attempts to improve the antitumor potency of anti-PD-1+CTLA-4 ICB by including additional therapeutics have been largely discouraged due to concerns of further increasing fatal toxicity. Here, we screened ∼3,000 Food and Drug Administration (FDA)-approved drugs and identified clofazimine as a potential third agent to optimize anti-PD-1+CTLA-4 ICB. Remarkably, clofazimine outperforms ICB dose reduction or steroid treatment in reversing lethality of irAEs, but unlike the detrimental effect of steroids on antitumor efficacy, clofazimine potentiates curative responses in anti-PD-1+CTLA-4 ICB. Mechanistically, clofazimine promotes E2F1 activation in CD8+ T cells to overcome resistance and counteracts pathogenic Th17 cells to abolish irAEs. Collectively, clofazimine potentiates the antitumor efficacy of anti-PD-1+CTLA-4 ICB, curbs intractable irAEs, and may fill a desperate clinical need to improve patient survival.

A single cell atlas of sexual development in Plasmodium falciparum

The developmental decision made by malaria parasites to become sexual underlies all malaria transmission. Here, we describe a rich atlas of short- and long-read single-cell transcriptomes of over 37,000 Plasmodium falciparum cells across intraerythrocytic asexual and sexual development. We used the atlas to explore transcriptional modules and exon usage along sexual development and expanded it to include malaria parasites collected from four Malian individuals naturally infected with multiple P. falciparum strains. We investigated genotypic and transcriptional heterogeneity within and among these wild strains at the single-cell level, finding differential expression between different strains even within the same host. These data are a key addition to the Malaria Cell Atlas interactive data resource, enabling a deeper understanding of the biology and diversity of transmission stages.

A human neural crest model reveals the developmental impact of neuroblastoma-associated chromosomal aberrations

Early childhood tumours arise from transformed embryonic cells, which often carry large copy number alterations (CNA). However, it remains unclear how CNAs contribute to embryonic tumourigenesis due to a lack of suitable models. Here we employ female human embryonic stem cell (hESC) differentiation and single-cell transcriptome and epigenome analysis to assess the effects of chromosome 17q/1q gains, which are prevalent in the embryonal tumour neuroblastoma (NB). We show that CNAs impair the specification of trunk neural crest (NC) cells and their sympathoadrenal derivatives, the putative cells-of-origin of NB. This effect is exacerbated upon overexpression of MYCN, whose amplification co-occurs with CNAs in NB. Moreover, CNAs potentiate the pro-tumourigenic effects of MYCN and mutant NC cells resemble NB cells in tumours. These changes correlate with a stepwise aberration of developmental transcription factor networks. Together, our results sketch a mechanistic framework for the CNA-driven initiation of embryonal tumours.

Challenges and best practices in omics benchmarking

Technological advances enabling massively parallel measurement of biological features - such as microarrays, high-throughput sequencing and mass spectrometry - have ushered in the omics era, now in its third decade. The resulting complex landscape of analytical methods has naturally fostered the growth of an omics benchmarking industry. Benchmarking refers to the process of objectively comparing and evaluating the performance of different computational or analytical techniques when processing and analysing large-scale biological data sets, such as transcriptomics, proteomics and metabolomics. With thousands of omics benchmarking studies published over the past 25 years, the field has matured to the point where the foundations of benchmarking have been established and well described. However, generating meaningful benchmarking data and properly evaluating performance in this complex domain remains challenging. In this Review, we highlight some common oversights and pitfalls in omics benchmarking. We also establish a methodology to bring the issues that can be addressed into focus and to be transparent about those that cannot: this takes the form of a spreadsheet template of guidelines for comprehensive reporting, intended to accompany publications. In addition, a survey of recent developments in benchmarking is provided as well as specific guidance for commonly encountered difficulties.

Droplet-Based Glycan and RNA Sequencing for Profiling the Distinct Cellular Glyco-States in Single Cells

Plate-based single-cell glycan and RNA sequencing (scGR-seq) is previously developed to realize the integrated analysis of glycome and transcriptome in single cells. However, the sample size is limited to only a few hundred cells. Here, a droplet-based scGR-seq is developed to address this issue by adopting a 10x Chromium platform to simultaneously profile ten thousand cells' glycome and transcriptome in single cells. To establish droplet-based scGR-seq, a comparative analysis of two distinct cell lines is performed: pancreatic ductal adenocarcinoma cells and normal pancreatic duct cells. Droplet-based scGR-seq revealed distinct glycan profiles between the two cell lines that showed a strong correlation with the results obtained by flow cytometry. Next, droplet-based scGR-seq is applied to a more complex sample: peripheral blood mononuclear cells (PBMC) containing various immune cells. The method can systematically map the glycan signature for each immune cell in PBMC as well as glycan alterations by cell lineage. Prediction of the association between the glycan expression and the gene expression using regression analysis ultimately leads to the identification of a glycan epitope that impacts cellular functions. In conclusion, the droplet-based scGR-seq realizes the high-throughput profiling of the distinct cellular glyco-states in single cells.

Steroids-producing nodules: a two-layered adrenocortical nodular structure as a precursor lesion of cortisol-producing adenoma

BACKGROUND

The human adrenal cortex consists of three functionally and structurally distinct layers; zona glomerulosa, zona fasciculata (zF), and zona reticularis (zR), and produces adrenal steroid hormones in a layer-specific manner; aldosterone, cortisol, and adrenal androgens, respectively. Cortisol-producing adenomas (CPAs) occur mostly as a result of somatic mutations associated with the protein kinase A pathway. However, how CPAs develop after adrenocortical cells acquire genetic mutations, remains poorly understood.

METHODS

We conducted integrated approaches combining the detailed histopathologic studies with genetic, RNA-sequencing, and spatially resolved transcriptome (SRT) analyses for the adrenal cortices adjacent to human adrenocortical tumours.

FINDINGS

Histopathological analysis revealed an adrenocortical nodular structure that exhibits the two-layered zF- and zR-like structure. The nodular structures harbour GNAS somatic mutations, known as a driver mutation of CPAs, and confer cell proliferative and autonomous steroidogenic capacities, which we termed steroids-producing nodules (SPNs). RNA-sequencing coupled with SRT analysis suggests that the expansion of the zF-like structure contributes to the formation of CPAs, whereas the zR-like structure is characterised by a macrophage-mediated immune response.

INTERPRETATION

We postulate that CPAs arise from a precursor lesion, SPNs, where two distinct cell populations might contribute differently to adrenocortical tumorigenesis. Our data also provide clues to the molecular mechanisms underlying the layered structures of human adrenocortical tissues.

FUNDING

KAKENHI, The Uehara Memorial Foundation, Daiwa Securities Health Foundation, Kaibara Morikazu Medical Science Promotion Foundation, Secom Science and Technology Foundation, ONO Medical Research Foundation, and Japan Foundation for Applied Enzymology.

Prediction of immunotherapy responsiveness in melanoma through single-cell sequencing-based characterization of the tumor immune microenvironment

Immune checkpoint inhibitors (ICB) therapy have emerged as effective treatments for melanomas. However, the response of melanoma patients to ICB has been highly heterogenous. Here, by analyzing integrated scRNA-seq datasets from melanoma patients, we revealed significant differences in the TiME composition between ICB-resistant and responsive tissues, with resistant or responsive tissues characterized by an abundance of myeloid cells and CD8+ T cells or CD4+ T cell predominance, respectively. Among CD4+ T cells, CD4+ CXCL13+ Tfh-like cells were associated with an immunosuppressive phenotype linked to immune escape-related genes and negative regulation of T cell activation. We also develop an immunotherapy response prediction model based on the composition of the immune compartment. Our predictive model was validated using CIBERSORTx on bulk RNA-seq datasets from melanoma patients pre- and post-ICB treatment and showed a better performance than other existing models. Our study presents an effective immunotherapy response prediction model with potential for further translation, as well as underscores the critical role of the TiME in influencing the response of melanomas to immunotherapy.

scLENS: data-driven signal detection for unbiased scRNA-seq data analysis

High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning.

Modeling gene expression cascades during cell state transitions

During cellular processes such as differentiation or response to external stimuli, cells exhibit dynamic changes in their gene expression profiles. Single-cell RNA sequencing (scRNA-seq) can be used to investigate these dynamic changes. To this end, cells are typically ordered along a pseudotemporal trajectory which recapitulates the progression of cells as they transition from one cell state to another. We infer transcriptional dynamics by modeling the gene expression profiles in pseudotemporally ordered cells using a Bayesian inference approach. This enables ordering genes along transcriptional cascades, estimating differences in the timing of gene expression dynamics, and deducing regulatory gene interactions. Here, we apply this approach to scRNA-seq datasets derived from mouse embryonic forebrain and pancreas samples. This analysis demonstrates the utility of the method to derive the ordering of gene dynamics and regulatory relationships critical for proper cellular differentiation and maturation across a variety of developmental contexts.

Enhancer-promoter interactions are reconfigured through the formation of long-range multiway hubs as mouse ES cells exit pluripotency

Enhancers bind transcription factors, chromatin regulators, and non-coding transcripts to modulate the expression of target genes. Here, we report 3D genome structures of single mouse ES cells as they are induced to exit pluripotency and transition through a formative stage prior to undergoing neuroectodermal differentiation. We find that there is a remarkable reorganization of 3D genome structure where inter-chromosomal intermingling increases dramatically in the formative state. This intermingling is associated with the formation of a large number of multiway hubs that bring together enhancers and promoters with similar chromatin states from typically 5-8 distant chromosomal sites that are often separated by many Mb from each other. In the formative state, genes important for pluripotency exit establish contacts with emerging enhancers within these multiway hubs, suggesting that the structural changes we have observed may play an important role in modulating transcription and establishing new cell identities.

Integrating bulk and single-cell sequencing data to construct a Scissor+ dendritic cells prognostic model for predicting prognosis and immune responses in ESCC

Esophageal squamous cell carcinoma (ESCC) is characterized by molecular heterogeneity with various immune cell infiltration patterns, which have been associated with therapeutic sensitivity and resistance. In particular, dendritic cells (DCs) are recently discovered to be associated with prognosis and survival in cancer. However, how DCs differ among ESCC patients has not been fully comprehended. Recently, the advance of single-cell RNA sequencing (scRNA-seq) enables us to profile the cell types, states, and lineages in the heterogeneous ESCC tissues. Here, we dissect the ESCC tumor microenvironment at high resolution by integrating 192,078 single cells from 60 patients, including 4379 DCs. We then used Scissor, a method that identifies cell subpopulations from single-cell data that are associated bulk samples with genomic and clinical information, to stratify DCs into Scissorhi and Scissorlow subtypes. We applied the Scissorhi gene signature to stratify ESCC scRNAseq patient, and we found that PD-L1, TIGIT, PVR and IL6 ligand-receptor-mediated cell interactions existed mainly in Scissorhi patients. Finally, based on the Scissor results, we successfully developed a validated prognostic risk model for ESCC and further validated the reliability of the risk prediction model by recruiting 40 ESCC clinical patients. This information highlights the importance of these genes in assessing patient prognosis and may help in the development of targeted or personalized therapies for ESCC.

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq

We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.

Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing

Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

DELVE: feature selection for preserving biological trajectories in single-cell data

Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve .

Best practices for the execution, analysis, and data storage of plant single-cell/nucleus transcriptomics

Single-cell and single-nucleus RNA-sequencing technologies capture the expression of plant genes at an unprecedented resolution. Therefore, these technologies are gaining traction in plant molecular and developmental biology for elucidating the transcriptional changes across cell types in a specific tissue or organ, upon treatments, in response to biotic and abiotic stresses, or between genotypes. Despite the rapidly accelerating use of these technologies, collective and standardized experimental and analytical procedures to support the acquisition of high-quality data sets are still missing. In this commentary, we discuss common challenges associated with the use of single-cell transcriptomics in plants and propose general guidelines to improve reproducibility, quality, comparability, and interpretation and to make the data readily available to the community in this fast-developing field of research.

Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies

Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.

Terminally differentiated cytotoxic CD4+ T cells were clonally expanded in the brain lesion of radiation-induced brain injury

BACKGROUND

Accumulating evidence supports the involvement of adaptive immunity in the development of radiation-induced brain injury (RIBI). Our previous work has emphasized the cytotoxic function of CD8+ T cells in RIBI. In this study, we aimed to investigate the presence and potential roles of cytotoxic CD4+ T cells (CD4+ CTLs) in RIBI to gain a more comprehensive understanding of adaptive immunity in this context.

MAIN TEXT

Utilizing single-cell RNA sequencing (scRNA-seq), we analyzed 3934 CD4+ T cells from the brain lesions of four RIBI patients and identified six subclusters within this population. A notable subset, the cytotoxic CD4+ T cells (CD4+ CTLs), was marked with high expression of cytotoxicity-related genes (NKG7, GZMH, GNLY, FGFBP2, and GZMB) and several chemokine and chemokine receptors (CCL5, CX3CR1, and CCL4L2). Through in-depth pseudotime analysis, which simulates the development of CD4+ T cells, we observed that the CD4+ CTLs exhibited signatures of terminal differentiation. Their functions were enriched in protein serine/threonine kinase activity, GTPase regulator activity, phosphoprotein phosphatase activity, and cysteine-type endopeptidase activity involved in the apoptotic signaling pathway. Correspondingly, mice subjected to gamma knife irradiation on the brain showed a time-dependent infiltration of CD4+ T cells, an increase of MHCII+ cells, and the existence of CD4+ CTLs in lesions, along with an elevation of apoptotic-related proteins. Finally, and most crucially, single-cell T-cell receptor sequencing (scTCR-seq) analysis at the patient level determined a large clonal expansion of CD4+ CTLs in lesion tissues of RIBI. Transcriptional factor-encoding genes TBX21, RORB, and EOMES showed positive correlations with the cytotoxic functions of CD4+ T cells, suggesting their potential to distinguish RIBI-related CD4+ CTLs from other subsets.

CONCLUSION

The present study enriches the understanding of the transcriptional landscape of adaptive immune cells in RIBI patients. It provides the first description of a clonally expanded CD4+ CTL subset in RIBI lesions, which may illuminate new mechanisms in the development of RIBI and offer potential biomarkers or therapeutic targets for the disease.

Circulating Tumor Reactive KIR+CD8+ T cells Suppress Anti-Tumor Immunity in Patients with Melanoma

Effective anti-tumor immunity is largely driven by cytotoxic CD8+ T cells that can specifically recognize tumor antigens. However, the factors which ultimately dictate successful tumor rejection remain poorly understood. Here we identify a subpopulation of CD8+ T cells which are tumor antigen-specific in patients with melanoma but resemble KIR+CD8+ T cells with a regulatory function (Tregs). These tumor antigen-specific KIR+CD8+ T cells are detectable in both the tumor and the blood, and higher levels of this population are associated with worse overall survival. Our findings therefore suggest that KIR+CD8+ Tregs are tumor antigen-specific but uniquely suppress anti-tumor immunity in patients with melanoma.

The single-cell transcriptome program of nodule development cellular lineages in Medicago truncatula

Legumes establish a symbiotic relationship with nitrogen-fixing rhizobia by developing nodules. Nodules are modified lateral roots that undergo changes in their cellular development in response to bacteria, but the transcriptional reprogramming that occurs in these root cells remains largely uncharacterized. Here, we describe the cell-type-specific transcriptome response of Medicago truncatula roots to rhizobia during early nodule development in the wild-type genotype Jemalong A17, complemented with a hypernodulating mutant (sunn-4) to expand the cell population responding to infection and subsequent biological inferences. The analysis identifies epidermal root hair and stele sub-cell types associated with a symbiotic response to infection and regulation of nodule proliferation. Trajectory inference shows cortex-derived cell lineages differentiating to form the nodule primordia and, posteriorly, its meristem, while modulating the regulation of phytohormone-related genes. Gene regulatory analysis of the cell transcriptomes identifies new regulators of nodulation, including STYLISH 4, for which the function is validated.

Comparative transcriptomics coupled to developmental grading via transgenic zebrafish reporter strains identifies conserved features in neutrophil maturation

Neutrophils are evolutionarily conserved innate immune cells playing pivotal roles in host defense. Zebrafish models have contributed substantially to our understanding of neutrophil functions but similarities to human neutrophil maturation have not been systematically characterized, which limits their applicability to studying human disease. Here we show, by generating and analysing transgenic zebrafish strains representing distinct neutrophil differentiation stages, a high-resolution transcriptional profile of neutrophil maturation. We link gene expression at each stage to characteristic transcription factors, including C/ebp-β, which is important for late neutrophil maturation. Cross-species comparison of zebrafish, mouse, and human samples confirms high molecular similarity of immature stages and discriminates zebrafish-specific from pan-species gene signatures. Applying the pan-species neutrophil maturation signature to RNA-sequencing data from human neuroblastoma patients reveals association between metastatic tumor cell infiltration in the bone marrow and an overall increase in mature neutrophils. Our detailed neutrophil maturation atlas thus provides a valuable resource for studying neutrophil function at different stages across species in health and disease.

Single-cell transcriptomics identifies the differentiation trajectory from inflammatory monocytes to pro-resolving macrophages in a mouse skin allergy model

Both monocytes and macrophages are heterogeneous populations. It was traditionally understood that Ly6Chi classical (inflammatory) monocytes differentiate into pro-inflammatory Ly6Chi macrophages. Accumulating evidence has suggested that Ly6Chi classical monocytes can also differentiate into Ly6Clo pro-resolving macrophages under certain conditions, while their differentiation trajectory remains to be fully elucidated. The present study with scRNA-seq and flow cytometric analyses reveals that Ly6ChiPD-L2lo classical monocytes recruited to the allergic skin lesion sequentially differentiate into Ly6CloPD-L2hi pro-resolving macrophages, via intermediate Ly6ChiPD-L2hi macrophages but not Ly6Clo non-classical monocytes, in an IL-4 receptor-dependent manner. Along the differentiation, classical monocyte-derived macrophages display anti-inflammatory signatures followed by metabolic rewiring concordant with their ability to phagocytose apoptotic neutrophils and allergens, therefore contributing to the resolution of inflammation. The failure in the generation of these pro-resolving macrophages drives the IL-1α-mediated cycle of inflammation with abscess-like accumulation of necrotic neutrophils. Thus, we clarify the stepwise differentiation trajectory from Ly6Chi classical monocytes toward Ly6Clo pro-resolving macrophages that restrain neutrophilic aggravation of skin allergic inflammation.

Interferon-stimulated neutrophils as a predictor of immunotherapy response

Despite the remarkable success of anti-cancer immunotherapy, its effectiveness remains confined to a subset of patients-emphasizing the importance of predictive biomarkers in clinical decision-making and further mechanistic understanding of treatment response. Current biomarkers, however, lack the power required to accurately stratify patients. Here, we identify interferon-stimulated, Ly6Ehi neutrophils as a blood-borne biomarker of anti-PD1 response in mice at baseline. Ly6Ehi neutrophils are induced by tumor-intrinsic activation of the STING (stimulator of interferon genes) signaling pathway and possess the ability to directly sensitize otherwise non-responsive tumors to anti-PD1 therapy, in part through IL12b-dependent activation of cytotoxic T cells. By translating our pre-clinical findings to a cohort of patients with non-small cell lung cancer and melanoma (n = 109), and to public data (n = 1440), we demonstrate the ability of Ly6Ehi neutrophils to predict immunotherapy response in humans with high accuracy (average AUC ≈ 0.9). Overall, our study identifies a functionally active biomarker for use in both mice and humans.