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

Impaired Aire-dependent IFN signaling in the thymus precedes the protective autoantibodies to IFNα

Recent studies have highlighted the role of the thymus in maintaining immune tolerance to type 1 interferons (T1 IFNs). Individuals with thymic abnormalities, such as autoimmune regulator (AIRE) gene mutations, frequently develop neutralizing autoantibodies to interferon-alpha (IFNα). Unlike mice, Aire-deficient rats develop robust autoantibodies to IFNα. Using this rat model, we show that Aire regulates the thymic expression of interferon-stimulated genes (ISGs), which occurs before developing anti-IFNα autoantibodies. In the periphery, we observed a widespread downregulation of ISGs across immune cells and reduced activation of natural killer (NK) cells. Furthermore, the presence of anti-IFNα autoantibodies correlated with reduced peripheral tissue inflammation, suggesting their role in dampening T1 IFN signaling and minimizing tissue infiltration. Our findings reveal that Aire-mediated regulation of thymic T1 IFN signaling is linked to the production of protective anti-IFNα autoantibodies, which inversely correlate with autoimmune pathology in peripheral tissues.

A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics

Single-cell RNA-seq (scRNAseq) struggles to capture the cellular heterogeneity of transcripts within individual cells due to the prevalence of highly abundant and ubiquitous transcripts, which can obscure the detection of biologically distinct transcripts expressed up to several orders of magnitude lower levels. To address this challenge, here we introduce single-cell CRISPRclean (scCLEAN), a molecular method that globally recomposes scRNAseq libraries, providing a benefit that cannot be recapitulated with deeper sequencing. scCLEAN utilizes the programmability of CRISPR/Cas9 to target and remove less than 1% of the transcriptome while redistributing approximately half of reads, shifting the focus toward less abundant transcripts. We experimentally apply scCLEAN to both heterogeneous immune cells and homogenous vascular smooth muscle cells to demonstrate its ability to uncover biological signatures in different biological contexts. We further emphasize scCLEAN's versatility by applying it to a third-generation sequencing method, single-cell MAS-Seq, to increase transcript-level detection and discovery. Here we show the possible utility of scCLEAN across a wide array of human tissues and cell types, indicating which contexts this technology proves beneficial and those in which its application is not advisable.

Mineralocorticoid receptor phase separation modulates cardiac preservation

Heart transplantation is the gold standard treatment for patients with end-stage heart failure. However, there is a shortage of donor hearts available. The short tolerable cold ischemic time for delivering donor hearts to matching recipients is closely responsible for this shortage. Here we uncover the phenomenon of mineralocorticoid receptor (MR) phase separation, which exacerbates injury to the murine and human donor heart during cold storage and can be modulated with pharmacological inhibition to improve preservation quality. Interestingly, donor cardiomyocytes strongly expressed MR, which undergoes preservation-related phase separation. The phenomenon of macromolecular phase separation is not limited to the heart or MR during preservation. Cold preservation of the lung, liver and kidney also displays phase separation of other transcriptional regulators including histone deacetylase 1 (HDAC1), bromodomain-containing 4 (BRD4) and MR. Our results reveal an understudied area of preservation biology that may be further exploited to improve the preservation of multiple solid organs.

Time-resolved single-cell atlas identifies the spatiotemporal transcription dynamics in vernalization response in Brassica rapa

Many temperate plants require vernalization, a prolonged low-temperature period, to accelerate flowering. Vernalization is a quantitative process whereby extended cold exposure establishes a stable transcriptional repression, with the degree of silencing correlating with the length of cold treatment. While much is known about the genes regulating this process, the expression dynamics at the single-cell level remain elusive. Using single-cell RNA sequencing, we analyze the vernalization response in Brassica rapa. Our data show that mesophyll cells exhibit the most significant changes in gene expression at low temperatures, whereas vasculature exhibits higher expression levels of flowering-related genes. Mesophyll trajectory analyses suggest that B. rapa plants undergo a biphasic response to chill stress during vernalization. Tissue-wide BrFLC expression changes result from variations in the proportion of expressing cells, supporting the quantitative nature of vernalization through digital cell responses. This study provides valuable resources and insights into the spatiotemporal regulation of flowering during vernalization.

Direct microglia replacement reveals pathologic and therapeutic contributions of brain macrophages to a monogenic neurological disease

Krabbe disease, also named globoid cell (GC) leukodystrophy (GLD) for its distinct lipid-laden macrophages, is a severe leukodystrophy caused by galactosylceramidase (GALC) mutations. Hematopoietic stem cell transplant (HSCT) ameliorates disease and is associated with central nervous system (CNS) engraftment of GALC+ donor macrophages. Yet, the role of macrophages in GLD pathophysiology and HSCT remains unclear. Using single-cell sequencing, we revealed early interferon response signatures that preceded progressively severe macrophage dyshomeostasis and identified a molecular signature of GCs, which we validated in human brain specimens. Genetic depletion and direct microglia replacement by CNS monocyte injection rapidly replaced >80% of endogenous microglia with healthy macrophages in the twitcher (GalcW355∗) mouse model of GLD. Perinatal microglia replacement completely normalized transcriptional signatures, rescued histopathology, and doubled average survival. Overall, we uncovered distinct forms of microglial dysfunction and evidence that direct, CNS-limited microglia replacement improves a monogenic neurodegenerative disease, identifying a promising therapeutic target.

Post-gastrulation amnioids as a stem cell-derived model of human extra-embryonic development

The amnion, an extra-embryonic tissue in mammalian embryos, is thought to provide crucial signaling, structural, and nutritional support during pregnancy. Despite its pivotal importance, studying human amnion formation and function has been hampered by the lack of accurate in vitro models. Here, we present an embryonic stem cell-derived 3D model of the post-gastrulation amnion, post-gastrulation amnioids (PGAs), that faithfully recapitulates extra-embryonic development up to 4 weeks post-fertilization, closely mimicking the functional traits of the human amniotic sac. PGAs self-organize, forming the amnion and the yolk sac, and are surrounded by the extra-embryonic mesoderm. Using PGAs, we show that GATA3 is required and sufficient for amniogenesis and that an autoregulatory feedback loop governs amnion formation, whereby extra-embryonic signals promote amnion specification. The reproducibility and scalability of the PGA system, with its precise cellular, structural, and functional integrity, opens avenues for investigating embryo-amnion interactions beyond gastrulation and offers an ideal platform for large-scale pharmacological and clinical studies.

Blocking cancer-fibroblast mutualism inhibits proliferation of endocrine therapy resistant breast cancer

In early-stage estrogen receptor-positive (ER + ) breast cancer, resistance to endocrine therapy (ET) and CDK4/6 inhibitors (CDK4/6i) often involve a shift away from estrogen-driven proliferation. The nature and source of compensatory growth signals driving cancer proliferation remain unknown but represent direct therapeutic targets of resistant cells. By analyzing single-cell RNA-sequencing data from serial biopsies of patient tumors, we elucidated compensatory growth signaling pathways activated in ET + CDK4/6i-resistant cancer cells, along with the intercellular growth signal communications within the tumor microenvironment. In most patient tumors, resistant cancer cells increased ERBB growth pathway activity during treatment, only partially through ERBB receptor upregulation. Concurrently, fibroblasts within the tumor increased ERBB ligand communication with cancer cells, as they differentiated to a proliferative and mesenchymal phenotype in response to TGF β signals from cancer cells. In vitro model systems demonstrated molecularly how therapy induces a mutualistic cycle of crosstalk between cancer cells and fibroblasts, fostering a growth factor-rich tumor microenvironment circumventing estrogen reliance. We show that ERBB inhibition can break this cancer-fibroblasts mutualism, targeting an acquired sensitivity of resistant cancer cells.

Distinct type I and II interferon responses direct cortical and medullary thymic epithelial cell development

Advances in genomics have redefined our understanding of thymic epithelial heterogeneity and architecture, yet signals driving thymic epithelial differentiation remain incompletely understood. Here, we elucidated pathways instructing human thymic epithelial cell development in the context of other anterior foregut-derived organs. Activation of interferon response gene regulatory networks distinguished epithelial cells of the thymus from those of other anterior foregut-derived organs. Thymic cortex and medulla epithelia displayed distinctive interferon-responsive signatures defined by lineage-specific chromatin accessibility. We explored the effects of type I and II interferons on thymic epithelial progenitor differentiation from induced pluripotent stem cells. Type II interferon was essential for expressing proteasome and antigen-presenting molecules, whereas type I or II interferons were essential for inducing different cytokines in thymic epithelial progenitor cells. Our findings suggest that interferons are critical to cortical and medullary thymic epithelial cell differentiation.

Presence of Tertiary Lymphoid Structures and Exhausted Tissue-Resident T Cells Determines Clinical Response to PD-1 Blockade in Renal Cell Carcinoma

SIGNIFICANCE

We describe a paradigm wherein combined high TLS and low tissue-resident exhausted CD8+ T cells are required for optimal response to PD-1 blockade in RCC. This analysis identifies key determinants of response to PD-1 blockade in advanced RCC and suggests avenues for future immune modulation through rational combination therapy strategies.

Transcriptional landscapes underlying Notch-induced lineage conversion and plasticity of mammary basal cells

The mammary epithelium derives from multipotent mammary stem cells (MaSCs) that engage into differentiation during embryonic development. However, adult MaSCs maintain the ability to reactivate multipotency in non-physiological contexts. We previously reported that Notch1 activation in committed basal cells triggers a basal-to-luminal cell fate switch in the mouse mammary gland. Here, we report conservation of this mechanism and found that in addition to the mammary gland, constitutive Notch1 signaling induces a basal-to-luminal cell fate switch in adult cells of the lacrimal gland, the salivary gland, and the prostate. Since the lineage transition is progressive in time, we performed single-cell transcriptomic analysis on index-sorted mammary cells at different stages of lineage conversion, generating a temporal map of changes in cell identity. Combining single-cell analyses with organoid assays, we demonstrate that cell proliferation is indispensable for this lineage conversion. We also reveal the individual transcriptional landscapes underlying the cellular plasticity switching of committed mammary cells in vivo with spatial and temporal resolution. Given the roles of Notch signaling in cancer, these results may help to better understand the mechanisms that drive cellular transformation.

Endothelial-to-mesenchymal transition gene signature derived from single-cell transcriptomics of human atherosclerotic tissue associates with stable plaque histological characteristics

BACKGROUND

Endothelial cells within atherosclerotic plaques can differentiate into a mesenchymal-like phenotype through endothelial-to-mesenchymal transition (EndoMT). Our understanding of the molecular mechanisms underlying EndoMT in human atherosclerosis remains limited. Current gene expression signatures are often derived from in vitro experiments or animal studies and typically reflect genes upregulated in fully differentiated mesenchymal cell states, while genes upregulated during the process are omitted. To address this knowledge gap, we utilized in silico lineage tracing in single-cell transcriptomic (scRNA-seq) data from human plaque tissues to identify the EndoMT gene expression signature.

METHODS AND RESULTS

We constructed three candidate EndoMT lineages across subpopulations of ECs and SMCs in human carotid scRNA-seq data (n = 46). We examined gene expression over the course of these lineages and identified a core signature of 73 genes upregulated in EndoMT. Upregulation of those genes was confirmed in EndoMT trajectories of other human datasets derived from plaque tissue and in Cdh5-CreERT2 Rosa-eYFP apoE-/- lineage-traced mice. Analysis of human carotid plaque bulk RNA-seq data (632 patients) found the association of core gene signature with fibrous and more stable histological phenotypes.

CONCLUSION

This study defines the core gene signature of EndoMT in human atherosclerotic plaques, which can serve as a reference for future studies and gene set enrichment analysis.

STAT3 signaling enhances tissue expansion during postimplantation mouse development

Signal transducer and activator of transcription (STAT)3 signaling has been studied extensively using mouse embryonic stem cells. Zygotic deletion of Stat3 enables embryo implantation, but thereafter, mutants resemble non-affected littermates from the previous day until around mid-gestation. This probably results from the loss of serine-phosphorylated STAT3, the predominant form in early postimplantation embryonic tissues associated with mitochondrial activity. Bulk RNA sequencing of isolated mouse epiblasts confirmed developmental delay transcriptionally. Single-cell RNA sequencing revealed the exclusion of derivatives of Stat3 null embryonic stem cells exclusively from the erythroid lineage of mid-gestation chimeras. We show that Stat3 null embryonic stem cells can differentiate into erythroid and hematopoietic lineages in vitro but become outcompeted when mixed with wild-type cells. Our results implicate a role for STAT3 in the temporal control of embryonic progression, particularly in tissues requiring rapid cell division, such as postimplantation epiblast and hematopoietic lineages. Interestingly, mutations in STAT3 are associated with short stature in humans.

Polarity-guided uneven mitotic divisions control brassinosteroid activity in proliferating plant root cells

Brassinosteroid hormones are positive regulators of plant organ growth, yet their function in proliferating tissues remains unclear. Here, through integrating single-cell RNA sequencing with long-term live-cell imaging of the Arabidopsis root, we reveal that brassinosteroid activity fluctuates throughout the cell cycle, decreasing during mitotic divisions and increasing during the G1 phase. The post-mitotic recovery of brassinosteroid activity is driven by the intrinsic polarity of the mother cell, resulting in one daughter cell with enhanced brassinosteroid signaling, while the other supports brassinosteroid biosynthesis. The coexistence of these distinct daughter cell states during the G1 phase circumvents a negative feedback loop to facilitate brassinosteroid production while signaling increases. Our findings uncover polarity-guided, uneven mitotic divisions in the meristem, which control brassinosteroid hormone activity to ensure optimal root growth.

Single cell RNAseq to identify subpopulations of glial progenitors in iPSC-derived oligodendroglial lineage cultures

Cellular heterogeneity is a common issue in differentiation protocols of oligodendrocytes (OLs) from human induced pluripotent stem cells. Our previous work described a novel method to generate OLs and highlighted the presence of glial progenitors. Here, we unravel the glial heterogeneity and characterize the response of isolated subpopulations to differentiation. This study provides a novel tool for studying the dynamics of glial development in vitro and on a transcriptomic level.

Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses

Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.

A single-cell approach to analyzing vascular endothelial cell contributions in VEGF-driven angiogenesis and LINC02313 in gastric cancer

Gastric cancer (GC) heterogeneity and lack of suitable molecular markers remain major challenges for this disease. The critical role of long non-coding RNAs (lncRNAs) in cancer biological processes has been increasingly recognized. A novel lncRNA, LINC02313, was identified in GC in this study, and its function was examined bioinformatically. The differential expression of LINC02313 was examined, and its target genes were predicted using RNA-Seq data from TCGA. LINC02313 showed correlation with 272 significant DEGs in GC. The analysis of single-cell transcriptomes revealed 11 unique clusters of cell types, but vascular endothelial cells have the most targets (30 genes). Receiver Operating Characteristic (ROC) analysis illustrated the diagnostic capabilities of LINC02313 and its targets across most cellular clusters, achieving the highest levels of accuracy. Functionally related signaling pathways were classified through cell-cell communication analysis; in the tumorous state, emphasizing the more prominent role of vascular endothelial cells in the Vascular Endothelial Growth Factor (VEGF) signaling pathway compared to the normal state. Trajectory analysis showed vascular endothelial cells are at the start of pseudotime in a normal state, but in a tumorous state, they shift to the middle of pseudotime. The results of this study highlight the critical role of endothelial cells in the advancement of GC and propose novel therapeutic approaches that focus on modulating angiogenic signaling pathways and lncRNA function to enhance treatment efficacy.

X-Linked Gene Dosage and SOX2 Act as Key Roadblocks for Human Germ Cell Specification in Klinefelter Syndrome

Klinefelter syndrome (KS), characterized by the presence of at least one extra X-chromosome, is a common cause of male infertility. However, the mechanism underlying the failure of germline specification is not well studied. Intriguingly, the differentiation efficiency of female human pluripotent stem cells (hPSCs) is often lower than that of male. This study investigates how X-linked gene dosage affects human primordial germ cell-like cells (hPGCLCs) specification in both healthy and diseased conditions. This work reveals that X-linked genes play a multifaceted role against the fate competency to hPGCLCs, with escape genes IGSF1 and CHRDL1 inhibiting the TGF-beta/Activin A and BMP pathways, respectively. Notably, this work identifies a previously unrecognized role of SOX2, upregulated by the escape gene USP9X, elucidating a species-specific function in the mammalian germline. The USP9X-SOX2 regulatory axis profoundly influenced cellular metabolism, mitochondrial morphology, and progenitor competence in hPGCLCs specification. Furthermore, the inability to downregulate SOX2 and upregulate SOX17 in response to BMP signaling impedes downstream gene activation due to motif binding competition. These findings shed novel insights into the human germline specification by elucidating the divergent roles of SOX2 versus SOX17 in mammals, influenced by X-linked gene dosage effects. These results offer potential applications for improving the induction efficiency of hPGCLCs, facilitating disease mechanistic studies.

Single-cell analysis of the early Drosophila salivary gland reveals that morphogenetic control involves both the induction and exclusion of gene expression programs

How tissue shape and therefore function is encoded by the genome remains in many cases unresolved. The tubes of the salivary glands in the Drosophila embryo start from simple epithelial placodes, specified through the homeotic factors Scr/Hth/Exd. Previous work indicated that early morphogenetic changes are prepatterned by transcriptional changes, but an exhaustive transcriptional blueprint driving physical changes was lacking. We performed single-cell-RNAseq-analysis of FACS-isolated early placodal cells, making up less than 0.4% of cells within the embryo. Differential expression analysis in comparison to epidermal cells analyzed in parallel generated a repertoire of genes highly upregulated within placodal cells prior to morphogenetic changes. Furthermore, clustering and pseudotime analysis of single-cell-sequencing data identified dynamic expression changes along the morphogenetic timeline. Our dataset provides a comprehensive resource for future studies of a simple but highly conserved morphogenetic process of tube morphogenesis. Unexpectedly, we identified a subset of genes that, although initially expressed in the very early placode, then became selectively excluded from the placode but not the surrounding epidermis, including hth, grainyhead and tollo/toll-8. We show that maintaining tollo expression severely compromised the tube morphogenesis. We propose tollo is switched off to not interfere with key Tolls/LRRs that are expressed and function in the tube morphogenesis.

Spatial transcriptomics reveals tryptophan metabolism restricting maturation of intratumoral tertiary lymphoid structures

Tertiary lymphoid structures (TLSs) are ectopic lymphoid aggregates found in numerous cancers, often linked to enhanced immunotherapy responses and better clinical outcomes. However, the factors driving TLS maturation are not fully understood. Using near single-cell spatial transcriptomic mapping, we comprehensively profile TLSs under various maturation stages and their microenvironment in hepatocellular carcinoma (HCC). Based on their developmental trajectories, we classify immature TLSs into two groups: conforming and deviating TLSs. Our findings indicate that conforming TLSs, similar to mature TLSs, possess a niche function for immunotherapy responses, while deviating TLSs do not. We discover that the tryptophan-enriched metabolic microenvironment shaped by malignant cells contributes to the deviation of TLS maturation. Inhibiting tryptophan metabolism promotes intratumoral TLS maturation and enhances tumor control, synergizing with anti-PD-1 treatments. Therefore, promoting TLS maturation represents a potential strategy to improve antitumor responses and immunotherapy outcomes.

How to view the female reproductive tract through single-cell looking glasses

Single-cell technologies have emerged as an unprecedented tool for biologists and clinicians, allowing them to assess organs and tissues at the level of individual cells. In the field of women's reproductive biology, single-cell studies have provided insights into the cellular and molecular processes that regulate reproductive and obstetrical functions in health and disease. The knowledge that these studies generate is helping clinicians to improve the understanding and diagnosis of infertility related issues or pregnancy complications and to find new avenues for their treatment. However, navigating the expansive landscape of this type of transcriptomic data analysis represents a pivotal challenge in current research. Single cell RNA sequencing involves isolating cells into droplets, reverse transcribing RNA to generate complementary DNA, with each droplet content uniquely labeled by a barcode. Upon sequencing the complementary DNAs, the barcodes enable the reassignment of sequencing reads to individual droplets, facilitating the reconstruction of the cellular landscape of the sample obtained from a tissue or organ and beyond. Researchers, equipped with the metaphorical 'single-cell glasses,' must adequately choose from a plethora of strategies to dissect and interpret cellular information. Sophisticated algorithms and the decision-making process are often underestimated, resulting in artefactual or cumbersome interpreted results. Computational biologists apply and innovate computational tools designed to process, model, and interpret expansive datasets. The ramifications of their work extend far beyond the realm of data processing; they give shape to the outcome of analyses, playing a pivotal role in drawing meaningful conclusions from the wealth of information garnered. In this review, we describe the wide variety of approaches and analytical steps available with enough detail to gain a concise picture of what a complete examination of a single-cell dataset would be. We commence with a discussion on key points in experimental design, highlighting crucial questions one should consider. Following this, we delve into the various preprocessing and quality control steps essential for any single-cell dataset. The subsequent section offers a detailed guide on constructing a single-cell atlas, exploring nuances such as differential characteristics in visualization and clustering techniques, as well as strategies for assigning identity to cell populations through gene marker annotations. Moving beyond the creation of an atlas, we explore methods for investigating pathological conditions. This involves conducting cell population comparison tests between conditions and analyzing specific cell-to-cell communications and cellular differentiation trajectories in both health and disease scenarios. This work aims to furnish a newcomer researcher and/or clinician with essential guidelines to embark on a single-cell adventure without succumbing to common pitfalls. By bridging the gap between theory and practice, it facilitates the translation of single-cell technologies into clinically relevant applications. Throughout the manuscript, practical examples of its usage in women's reproductive health studies are provided. Various sections delve into specific clinical scenarios, demonstrating how these guidelines can be instrumental in unraveling the molecular landscapes of diseases and physiological processes related to women's reproduction.

CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data

Cell-cell communication is essential for tissue development, function and regeneration. The revolution of single-cell genomics technologies offers an unprecedented opportunity to uncover how cells communicate in vivo within their tissue niches and how disruption of these niches can lead to diseases and developmental abnormalities. CellPhoneDB is a bioinformatics toolkit designed to infer cell-cell communication by combining a curated repository of bona fide ligand-receptor interactions with methods to integrate these interactions with single-cell genomics data. Here we present a protocol for the latest version of CellPhoneDB (v5), offering several new features. First, the repository has been expanded by one-third with the addition of new interactions, including ~1,000 interactions mediated by nonpeptidic ligands such as steroidogenic hormones, neurotransmitters and small G-protein-coupled receptor (GPCR)-binding ligands. Second, we outline a new way of using the database that allows users to tailor queries to their experimental designs. Third, the update incorporates novel strategies to prioritize specific cell-cell interactions, leveraging information from other modalities such as tissue microenvironments derived from spatial transcriptomics technologies or transcription factor activities derived from a single-cell assay for transposase accessible chromatin assays. Finally, we describe the new CellPhoneDBViz module to interactively visualize and share results. Altogether, CellPhoneDB v5 enhances the precision of cell-cell communication inference, offering new insights into tissue biology in physiological microenvironments. This protocol typically takes ~15 min and requires basic knowledge of python.

Examining cellular heterogeneity in human DNA methylation studies: Overview and recommendations

Intersample cellular heterogeneity (ISCH) is one of the largest contributors to DNA methylation (DNAme) variability. It is imperative to account for ISCH to accurately interpret analysis results in epigenome-wide association studies. We compiled this primer based on the current literature to guide researchers through the process of estimating and accounting for ISCH in DNA methylation studies. This primer outlines the procedure of bioinformatic ISCH prediction, including using reference-based and reference-free algorithms. It then follows with descriptions of several methods to account for ISCH in downstream analyses, including robust linear regression and principal-component-analysis-based adjustments. Finally, we outlined three methods for estimating differential DNAme signals in a cell-type-specific manner. Throughout the primer, we provided statistical and biological justification for our recommendations, as well as R code examples for ease of implementation.

Population dynamics modeling reveals that myeloid bias involves both HSC differentiation and progenitor proliferation biases

ABSTRACT

Aging and chronic inflammation are associated with overabundant myeloid-primed multipotent progenitors (MPPs) among hematopoietic stem and progenitor cells (HSPCs). Although hematopoietic stem cell (HSC) differentiation bias has been considered a primary cause of myeloid bias, whether it is sufficient has not been quantitatively evaluated. Here, we analyzed bone marrow data from the IκB- (Nfkbia+/-Nfkbib-/-Nfkbie-/-) mouse model of inflammation with elevated NFκB activity, which reveals increased myeloid-biased MPPs. We interpreted these data with differential equation models of population dynamics to identify alterations of HSPC proliferation and differentiation rates. This analysis revealed that short-term HSC differentiation bias alone is likely insufficient to account for the increase in myeloid-biased MPPs. To explore additional mechanisms, we used single-cell RNA sequencing (scRNA-seq) measurements of IκB- and wild-type HSPCs to track the continuous differentiation trajectories from HSCs to erythrocyte/megakaryocyte, myeloid, and lymphoid primed progenitors. Fitting a partial differential equations model of population dynamics to these data revealed not only less lymphoid-fate specification among HSCs but also increased expansion of early myeloid-primed progenitors. Differentially expressed genes along the differentiation trajectories supported increased proliferation among these progenitors. These findings were conserved when wild-type HSPCs were transplanted into IκB- recipients, indicating that an inflamed bone marrow microenvironment is a sufficient driver. We then applied our analysis pipeline to scRNA-seq measurements of HSPCs isolated from aged mice and human patients with myeloid neoplasms. These analyses identified the same myeloid-primed progenitor expansion as in the IκB- models, suggesting that it is a common feature across different settings of myeloid bias.

Inhibiting EZH2 complements steroid effects in Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a devastating X-linked disorder caused by dystrophin gene mutations. Despite recent advances in understanding the disease etiology and applying emerging treatment methodologies, glucocorticoid derivatives remain the only general therapeutic option that can slow disease development. However, the precise molecular mechanism of glucocorticoid action remains unclear, and there is still need for additional remedies to complement the treatment. Here, using single-nucleus RNA sequencing and spatial transcriptome analyses of human and mouse muscles, we investigated pathogenic features in patients with DMD and palliative effects of glucocorticoids. Our approach further illuminated the importance of proliferating satellite cells and revealed increased activity of a signal transduction pathway involving EZH2 in the patient cells. Subsequent administration of EZH2 inhibitors to Dmd mutant mice resulted in improved muscle phenotype through maintaining the immune-suppressing effect but overriding the muscle weakness and fibrogenic effects exerted by glucocorticoids. Our analysis reveals pathogenic mechanisms that can be readily targeted by extant therapeutic options for DMD.

A Latent Activated Olfactory Stem Cell State Revealed by Single-Cell Transcriptomic and Epigenomic Profiling

The olfactory epithelium is one of the few regions of the nervous system that sustains neurogenesis throughout life. Its experimental accessibility makes it especially tractable for studying molecular mechanisms that drive neural regeneration in response to injury. In this study, we used single-cell sequencing to identify the transcriptional cascades and epigenetic processes involved in determining olfactory epithelial stem cell fate during injury-induced regeneration. By combining gene expression and accessible chromatin profiles of individual lineage-traced olfactory stem cells, we identified transcriptional heterogeneity among activated stem cells at a stage when cell fates are being specified. We further identified a subset of resting cells that appears poised for activation, characterized by accessible chromatin around wound response and lineage-specific genes prior to their later expression in response to injury. Together these results provide evidence for a latent activated stem cell state, in which a subset of quiescent olfactory epithelial stem cells are epigenetically primed to support injury-induced regeneration.

Alveolar macrophages from persons with HIV mount impaired TNF signaling networks to M. tuberculosis infection

People living with HIV (PLWH) have an increased risk for developing tuberculosis after M. tuberculosis infection, despite anti-retroviral therapy (ART). To delineate the underlying mechanisms, we conducted single cell transcriptomics on bronchoalveolar lavage cells from PLWH on ART and HIV uninfected healthy controls infected with M. tuberculosis ex vivo. We identify an M1-like proinflammatory alveolar macrophage subset that sequentially acquires TNF signaling capacity in controls but not in PLWH. Cell-cell communication analyses reveal interactions between M1-like macrophages and effector memory T cells within TNF superfamily, chemokine, and costimulatory networks in the airways of controls. These interaction networks were lacking in PLWH infected with M. tuberculosis, where anti-inflammatory M2-like alveolar macrophages and T regulatory cells dominated along with dysregulated T cell signatures. Our data support a model in which impaired TNF-TNFR signaling, M2-like alveolar macrophages and aberrant macrophage-T cell crosstalk, lead to ineffective immunity to M. tuberculosis in PLWH on ART.

Immunomodulatory role of the stem cell circadian clock in muscle repair

Circadian rhythms orchestrate physiological processes such as metabolism, immune function, and tissue regeneration, aligning them with the optimal time of day (TOD). This study identifies an interplay between the circadian clock within muscle stem cells (SCs) and their capacity to modulate the immune microenvironment during muscle regeneration. We reveal that the SC clock triggers TOD-dependent inflammatory gene transcription after injury, particularly genes related to neutrophil activity and chemotaxis. These responses are driven by cytosolic regeneration of the signaling metabolite nicotinamide adenine dinucleotide (oxidized form) (NAD+), as enhancing cytosolic NAD+ regeneration in SCs is sufficient to induce inflammatory responses that influence muscle regeneration. Mononuclear single-cell sequencing of the regenerating muscle niche further implicates the cytokine CCL2 in mediating SC-neutrophil cross-talk in a TOD-dependent manner. Our findings highlight the intersection between SC metabolic shifts and immune responses within the muscle microenvironment, dictated by circadian rhythms, and underscore the potential for targeting circadian and metabolic pathways to enhance tissue regeneration.

Continuous map of early hematopoietic stem cell differentiation across human lifetime

Uncovering early gene network changes of human hematopoietic stem cells (HSCs) leading to differentiation induction is of utmost importance for therapeutic manipulation. We employed single cell proteo-transcriptomic sequencing to FACS-enriched bone marrow hematopoietic stem and progenitor cells (HSPCs) from 15 healthy donors. Pseudotime analysis reveals four major differentiation trajectories, which remain consistent upon aging, with an early branching point into megakaryocyte-erythroid progenitors. However, young donors suggest a more productive differentiation from HSPCs to committed progenitors of all lineages. tradeSeq analysis depicts continuous changes in gene expression of HSPC-related genes (DLK1, ADGRG6), and provides a roadmap of gene expression at the earliest branching points. We identify CD273/PD-L2 to be highly expressed in a subfraction of immature multipotent HSPCs with enhanced quiescence. Functional experiments confirm the immune-modulatory function of CD273/PD-L2 on HSPCs in regulating T-cell activation and cytokine release. Here, we present a molecular map of early HSPC differentiation across human life.

Mouse totipotent blastomere-like cells model embryogenesis from zygotic genome activation to post implantation

Embryo development begins with zygotic genome activation (ZGA), eventually generating blastocysts for implantation. However, in vitro systems modeling the pre-implantation development are still absent and challenging. Here, we used mouse totipotent blastomere-like cells (TBLCs) to develop spontaneous differentiation and blastoid formation systems, respectively. We found Wnt signaling enabled the rapid expansion of TBLCs and the optimization of their culture medium. We successfully developed a TBLC-spontaneous differentiation system in which mouse TBLCs (mTBLCs) firstly converted into two types of ZGA-like cells (ZLCs) distinguished by Zscan4 expression. Surprisingly, Zscan4-, but not Zscan4+, ZLCs further passed through intermediate 4-cell and then 8-cell/morula stages to produce epiblast, primitive endoderm, and trophectoderm lineages. Significantly, single TBLCs underwent expansion, compaction, and polarization to efficiently generate blastocyst-like structures and even post-implantation egg-cylinder-like structures. Conclusively, we established TBLC-based differentiation and embryo-like structure formation systems to model early embryonic development, offering criteria for evaluating and understanding totipotency.

Exposing the cellular situation: findings from single cell RNA sequencing in breast cancer

Background

Breast Cancer (BC) ranks among the top three most prevalent cancers globally and stands as the principal contributor to cancer-related fatalities among women. In spite of the substantial occurrence rate of BC, the early stage of this disease is generally regarded as curable. However, intra-tumor heterogeneity presents a formidable obstacle to the success of effective treatment.

Method

In this research, single cell RNA sequencing was utilized to dissect the tumor microenvironment within BC. Slingshot, CytoTRACE and Monocle 2 were applied to illustrate the differentiation process of each subpopulation in the pseudotime sequence. To comprehensively comprehend the tumor cells (TCs) in BC, an analysis of upstream transcription factors was carried out via pySCENIC, while downstream pathway enrichment was conducted through KEGG, GO and GSEA. The prognosis model was established based on the bulk data obtained from TCGA and GEO databases. Knock-down experiments were also implemented to explore the function of the transcription factor CEBPD in the TCs.

Results

Our in-depth analysis identified eight principal cell types. Notably, TCs were predominantly found within epithelial cells. The classification of TCs further uncovered five unique subpopulations, with one subpopulation characterized by high UGDH expression. This subpopulation was shown to possess distinct metabolic features in metabolism-related investigations. The intricate communication modalities among different cell types were effectively demonstrated by means of CellChat. Additionally, a crucial transcription factor, CEBPD, was identified, which demonstrated a pronounced propensity towards tumors and harbored potential tumor-advancing characteristics. Its role in promoting cancer was subsequently verified through in vitro knock-down experiments. Moreover, a prognostic model was also developed, and a risk score was established based on the genes incorporated in the model. Through comparing the prognoses of different UTRS levels, it was determined that the group with a high UTRS had a less favorable prognosis.

Conclusion

These outcomes contributed to the elucidation of the complex interrelationships within the BC tumor microenvironment. By specifically targeting certain subpopulations of TCs, novel treatment strategies could potentially be devised. This study shed light on the direction that future research in BC should take, furnishing valuable information that can be utilized to enhance treatment regimens.

A multi-step completion process model of cell plasticity

Plasticity is the potential for cells or cell populations to change their phenotypes and behaviors in response to internal or external cues. Plasticity is fundamental to many complex biological processes, yet to date there remains a lack of mathematical models that can elucidate and predict molecular behaviors in a plasticity program. Here, we report a new mathematical framework that models cell plasticity as a multi-step completion process, where the system moves from the initial state along a path guided by multiple intermediate attractors until the final state (i.e. a new homeostasis) is reached. Using omics time-series data as model input, we show that our method fits data well; identifies attractor states by their timing and molecular markers which are well-aligned with domain knowledge; and can make quantitative and time-resolved predictions such as the molecular outcomes of blocking a plasticity program from reaching completion, to an R2 of 0.53-0.63. We demonstrate that application of our model to primary patient-derived data can provide quantitative insights and predictions that may be useful in guiding further research and potential biomedical interventions.

Distinct gene regulatory dynamics drive skeletogenic cell fate convergence during vertebrate embryogenesis

Cell type repertoires have expanded extensively in metazoan animals, with some clade-specific cells being crucial to evolutionary success. A prime example are the skeletogenic cells of vertebrates. Depending on anatomical location, these cells originate from three different precursor lineages, yet they converge developmentally towards similar cellular phenotypes. Furthermore, their 'skeletogenic competency' arose at distinct evolutionary timepoints, thus questioning to what extent different skeletal body parts rely on truly homologous cell types. Here, we investigate how lineage-specific molecular properties are integrated at the gene regulatory level, to allow for skeletogenic cell fate convergence. Using single-cell functional genomics, we find that distinct transcription factor profiles are inherited from the three precursor states and incorporated at lineage-specific enhancer elements. This lineage-specific regulatory logic suggests that these regionalized skeletogenic cells are distinct cell types, rendering them amenable to individualized selection, to define adaptive morphologies and biomaterial properties in different parts of the vertebrate skeleton.

TrAGEDy-trajectory alignment of gene expression dynamics

MOTIVATION

Single-cell transcriptomics sequencing is used to compare different biological processes. However, often, those processes are asymmetric which are difficult to integrate. Current approaches often rely on integrating samples from each condition before either cluster-based comparisons or analysis of an inferred shared trajectory.

RESULTS

We present Trajectory Alignment of Gene Expression Dynamics (TrAGEDy), which allows the alignment of independent trajectories to avoid the need for error-prone integration steps. Across simulated datasets, TrAGEDy returns the correct underlying alignment of the datasets, outperforming current tools which fail to capture the complexity of asymmetric alignments. When applied to real datasets, TrAGEDy captures more biologically relevant genes and processes, which other differential expression methods fail to detect when looking at the developments of T cells and the bloodstream forms of Trypanosoma brucei when affected by genetic knockouts.

AVAILABILITY AND IMPLEMENTATION

TrAGEDy is freely available at https://github.com/No2Ross/TrAGEDy, and implemented in R.

Cellular interactions within the immune microenvironment underpins resistance to cell cycle inhibition in breast cancers

Immune evasion by cancer cells involves reshaping the tumor microenvironment (TME) via communication with non-malignant cells. However, resistance-promoting interactions during treatment remain lesser known. Here we examine the composition, communication, and phenotypes of tumor-associated cells in serial biopsies from stage II and III high-risk estrogen receptor positive (ER+ ) breast cancers of patients receiving endocrine therapy (letrozole) as single agent or in combination with ribociclib, a CDK4/6-targeting cell cycle inhibitor. Single-cell RNA sequencing analyses on longitudinally collected samples show that in tumors overcoming the growth suppressive effects of ribociclib, first cancer cells upregulate cytokines and growth factors that stimulate immune-suppressive myeloid differentiation, resulting in reduced myeloid cell- CD8 + T-cell crosstalk via IL-15/18 signaling. Subsequently, tumors growing during treatment show diminished T-cell activation and recruitment. In vitro, ribociclib does not only inhibit cancer cell growth but also T cell proliferation and activation upon co-culturing. Exogenous IL-15 improves CDK4/6 inhibitor efficacy by augmenting T-cell proliferation and cancer cell killing by T cells. In summary, response to ribociclib in stage II and III high-risk ER +  breast cancer depends on the composition, activation phenotypes and communication network of immune cells.

Spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis

Large-scale changes in the structure and cellular makeup of the distal lung are a hallmark of pulmonary fibrosis (PF), but the spatial contexts that contribute to disease pathogenesis have remained uncertain. Using image-based spatial transcriptomics, we analyzed the gene expression of 1.6 million cells from 35 unique lungs. Through complementary cell-based and innovative cell-agnostic analyses, we characterized the localization of PF-emergent cell types, established the cellular and molecular basis of classical PF histopathologic features and identified a diversity of distinct molecularly defined spatial niches in control and PF lungs. Using machine learning and trajectory analysis to segment and rank airspaces on a gradient of remodeling severity, we identified compositional and molecular changes associated with progressive distal lung pathology, beginning with alveolar epithelial dysregulation and culminating with changes in macrophage polarization. Together, these results provide a unique, spatially resolved view of PF and establish methods that could be applied to other spatial transcriptomic studies.

Advancements in proteogenomics for preclinical targeted cancer therapy research

Advancements in molecular characterization technologies have accelerated targeted cancer therapy research at unprecedented resolution and dimensionality. Integrating comprehensive multi-omic molecular profiling of a tumor, proteogenomics, marks a transformative milestone for preclinical cancer research. In this paper, we initially provided an overview of proteogenomics in cancer research, spanning genomics, transcriptomics, and proteomics. Subsequently, the applications were introduced and examined from different perspectives, including but not limited to genetic alterations, molecular quantifications, single-cell patterns, different post-translational modification levels, subtype signatures, and immune landscape. We also paid attention to the combined multi-omics data analysis and pan-cancer analysis. This paper highlights the crucial role of proteogenomics in preclinical targeted cancer therapy research, including but not limited to elucidating the mechanisms of tumorigenesis, discovering effective therapeutic targets and promising biomarkers, and developing subtype-specific therapies.

Investigating biological nitrogen fixation via single-cell transcriptomics

The extensive use of nitrogen fertilizers has detrimental environmental consequences, and it is essential for society to explore sustainable alternatives. One promising avenue is engineering root nodule symbiosis, a naturally occurring process in certain plant species within the nitrogen-fixing clade, into non-leguminous crops. Advancements in single-cell transcriptomics provide unprecedented opportunities to dissect the molecular mechanisms underlying root nodule symbiosis at the cellular level. This review summarizes key findings from single-cell studies in Medicago truncatula, Lotus japonicus, and Glycine max. We highlight how these studies address fundamental questions about the development of root nodule symbiosis, including the following findings: (i) single-cell transcriptomics has revealed a conserved transcriptional program in root hair and cortical cells during rhizobial infection, suggesting a common infection pathway across legume species; (ii) characterization of determinate and indeterminate nodules using single-cell technologies supports the compartmentalization of nitrogen fixation, assimilation, and transport into distinct cell populations; (iii) single-cell transcriptomics data have enabled the identification of novel root nodule symbiosis genes and provided new approaches for prioritizing candidate genes for functional characterization; and (iv) trajectory inference and RNA velocity analyses of single-cell transcriptomics data have allowed the reconstruction of cellular lineages and dynamic transcriptional states during root nodule symbiosis.

scRDiT: Generating Single-cell RNA-seq Data by Diffusion Transformers and Accelerating Sampling

Single-cell RNA sequencing (scRNA-seq) is a groundbreaking technology extensively utilized in biological research, facilitating the examination of gene expression at the individual cell level within a given tissue sample. While numerous tools have been developed for scRNA-seq data analysis, the challenge persists in capturing the distinct features of such data and replicating virtual datasets that share analogous statistical properties. Our study introduces a generative approach termed scRNA-seq Diffusion Transformer (scRDiT). This method generates virtual scRNA-seq data by leveraging a real dataset. The method is a neural network constructed based on Denoising Diffusion Probabilistic Models (DDPMs) and Diffusion Transformers (DiTs). This involves subjecting Gaussian noises to the real dataset through iterative noise-adding steps and ultimately restoring the noises to form scRNA-seq samples. This scheme allows us to learn data features from actual scRNA-seq samples during model training. Our experiments, conducted on two distinct scRNA-seq datasets, demonstrate superior performance. Additionally, the model sampling process is expedited by incorporating Denoising Diffusion Implicit Models (DDIMs). scRDiT presents a unified methodology empowering users to train neural network models with their unique scRNA-seq datasets, enabling the generation of numerous high-quality scRNA-seq samples.

A developmental atlas of mouse vestibular-like inner ear organoids

Inner ear sensory epithelia can be generated in vitro from embryonic stem cells. The resulting inner ear organoids represent a potentially inexhaustible source of otic tissues, including sensory hair cells and supporting cells, for in vitro manipulation. Here, we present a single-cell atlas of whole mouse embryonic stem cell-derived vestibular-like inner ear organoids at six developmental stages. Our analyses trace the genesis and developmental progression of otic progenitor cells to supporting cells and hair cells. By profiling all organoid cells, we also characterize the development of additional cell groups, such as otic mesenchyme. We further utilize our atlas to describe a proliferative phase where otic progenitors produce hair cells and otic neuroblasts, followed by a transition to a non-proliferative state. The resulting map of otic progression reveals specific time windows that can inform future research on cell cycle regulation and cell lineage specification in inner ear organoids.

REG3A secreted by peritumoral acinar cells enhances pancreatic ductal adenocarcinoma progression via activation of EGFR signaling

BACKGROUND

Regenerating family member 3A (REG3A) is involved in the development of multiple malignant tumors, including pancreatic ductal adenocarcinoma (PDAC). However, any role of REG3A in PDAC remains controversial due to its unclear tissue localization or direct receptors, and complex downstream signal transductions.

METHODS

Morphological analysis and public multi-omics data retrieval were was utilized to elucidate the tissue localization of REG3A in PDAC. To ascertain the pro-oncogenic role of secreted REG3A, experiments were conducted using in vitro PDAC cell lines and in vivo tumor formation assays in nude mice. A battery of investigative techniques, including RNA sequencing, phospho-kinase arrays, western blot analyses, in silico docking simulations, gene truncation strategies, and co-immunoprecipitation, were employed to delve into the downstream signaling transduction pathways induced by REG3A.

RESULTS

In this study, we confirmed an association between increased serum levels of REG3A and poor prognosis in patients with PDAC. Morphological staining and bioinformatic analysis showed that REG3A was mainly expressed in peritumoral acinar cells that were spatially close to tumor region, while it was almost negative in PDAC tumor cells. Peritumoral REG3A expression levels, but not tumoral REG3A, were highly correlated with PDAC progression. Further in vitro experiments including RNA sequencing and molecular biological assays revealed that secreted REG3A could directly bind to the epidermal growth factor receptor (EGFR), an important pro-oncogene involved in cellular proliferation, and subsequently activate the downstream mitogen-activated protein kinase (MAPK) signals to promote PDAC tumor cell growth.

CONCLUSION

Taken together, our data indicated that increased expression of REG3A in peritumoral acinar cells acts as a specific event to indicate PDAC progression, and verified EGFR as a possible target of REG3A, providing mechanistic insights into the role of REG3A, the diagnostic method and therapeutic strategy of PDAC.

Towards understanding cancer dormancy over strategic hitching up mechanisms to technologies

Delving into cancer dormancy has been an inherent task that may drive the lethal recurrence of cancer after primary tumor relief. Cells in quiescence can survive for a short or long term in silence, may undergo genetic or epigenetic changes, and can initiate relapse through certain contextual cues. The state of dormancy can be induced by multiple conditions including cancer drug treatment, in turn, undergoes a life cycle that generally occurs through dissemination, invasion, intravasation, circulation, immune evasion, extravasation, and colonization. Throughout this cascade, a cellular machinery governs the fate of individual cells, largely affected by gene regulation. Despite its significance, a precise view of cancer dormancy is yet hampered. Revolutionizing advanced single cell and long read sequencing through analysis methodologies and artificial intelligence, the most recent stage in the research tool progress, is expected to provide a holistic view of the diverse aspects of cancer dormancy.

A Benchmarking Study of Random Projections and Principal Components for Dimensionality Reduction Strategies in Single Cell Analysis

Principal Component Analysis (PCA) has long been a cornerstone in dimensionality reduction for high-dimensional data, including single-cell RNA sequencing (scRNA-seq). However, PCA's performance typically degrades with increasing data size, can be sensitive to outliers, and assumes linearity. Recently, Random Projection (RP) methods have emerged as promising alternatives, addressing some of these limitations. This study systematically and comprehensively evaluates PCA and RP approaches, including Singular Value Decomposition (SVD) and randomized SVD, alongside Sparse and Gaussian Random Projection algorithms, with a focus on computational efficiency and downstream analysis effectiveness. We benchmark performance using multiple scRNA-seq datasets including labeled and unlabeled publicly available datasets. We apply Hierarchical Clustering and Spherical K-Means clustering algorithms to assess downstream clustering quality. For labeled datasets, clustering accuracy is measured using the Hungarian algorithm and Mutual Information. For unlabeled datasets, the Dunn Index and Gap Statistic capture cluster separation. Across both dataset types, the Within-Cluster Sum of Squares (WCSS) metric is used to assess variability. Additionally, locality preservation is examined, with RP outperforming PCA in several of the evaluated metrics. Our results demonstrate that RP not only surpasses PCA in computational speed but also rivals and, in some cases, exceeds PCA in preserving data variability and clustering quality. By providing a thorough benchmarking of PCA and RP methods, this work offers valuable insights into selecting optimal dimensionality reduction techniques, balancing computational performance, scalability, and the quality of downstream analyses.

paraCell: a novel software tool for the interactive analysis and visualization of standard and dual host-parasite single-cell RNA-seq data

Advances in sequencing technology have led to a dramatic increase in the number of single-cell transcriptomic datasets. In the field of parasitology, these datasets typically describe the gene expression patterns of a given parasite species at the single-cell level under experimental conditions, in specific hosts or tissues, or at different life cycle stages. However, while this wealth of available data represents a significant resource, analysing these datasets often requires expert computational skills, preventing a considerable proportion of the parasitology community from meaningfully integrating existing single-cell data into their work. Here, we present paraCell, a novel software tool that allows the user to visualize and analyse pre-loaded single-cell data without requiring any programming ability. The source code is free to allow remote installation. On our web server, we demonstrated how to visualize and re-analyse published Plasmodium and Trypanosoma datasets. We have also generated Toxoplasma-mouse and Theileria-cow scRNA-seq datasets to highlight the functionality of paraCell for pathogen-host interaction. The analysis of the data highlights the impact of the host interferon-γ response and gene expression profiles associated with disease susceptibility by these intracellular parasites, respectively.

Longitudinal single-cell profiles of lung regeneration after viral infection reveal persistent injury-associated cell states

Functional regeneration of the lung's gas exchange surface following injury requires the coordination of a complex series of cell behaviors within the alveolar niche. Using single-cell transcriptomics combined with lineage tracing of proliferating progenitors, we examined mouse lung regeneration after influenza injury, demonstrating an asynchronously phased response across different cellular compartments. This longitudinal atlas of injury responses has produced a catalog of transient and persistent transcriptional alterations in cells as they transit across axes of differentiation. These cell states include an injury-induced capillary endothelial cell (iCAP) that arises after injury, persists indefinitely, and shares hallmarks with developing lung endothelium and endothelial aberrations found in degenerative human lung diseases. This dataset provides a foundational resource to understand the complexity of cellular and molecular responses to injury and correlations to responses found in human development and disease.

Advancements in single-cell RNA sequencing and spatial transcriptomics: transforming biomedical research

In recent years, significant advancements in biochemistry, materials science, engineering, and computer-aided testing have driven the development of high-throughput tools for profiling genetic information. Single-cell RNA sequencing (scRNA-seq) technologies have established themselves as key tools for dissecting genetic sequences at the level of single cells. These technologies reveal cellular diversity and allow for the exploration of cell states and transformations with exceptional resolution. Unlike bulk sequencing, which provides population-averaged data, scRNA-seq can detect cell subtypes or gene expression variations that would otherwise be overlooked. However, a key limitation of scRNA-seq is its inability to preserve spatial information about the RNA transcriptome, as the process requires tissue dissociation and cell isolation. Spatial transcriptomics is a pivotal advancement in medical biotechnology, facilitating the identification of molecules such as RNA in their original spatial context within tissue sections at the single-cell level. This capability offers a substantial advantage over traditional single-cell sequencing techniques. Spatial transcriptomics offers valuable insights into a wide range of biomedical fields, including neurology, embryology, cancer research, immunology, and histology. This review highlights single-cell sequencing approaches, recent technological developments, associated challenges, various techniques for expression data analysis, and their applications in disciplines such as cancer research, microbiology, neuroscience, reproductive biology, and immunology. It highlights the critical role of single-cell sequencing tools in characterizing the dynamic nature of individual cells.

Gene trajectory inference for single-cell data by optimal transport metrics

Single-cell RNA sequencing has been widely used to investigate cell state transitions and gene dynamics of biological processes. Current strategies to infer the sequential dynamics of genes in a process typically rely on constructing cell pseudotime through cell trajectory inference. However, the presence of concurrent gene processes in the same group of cells and technical noise can obscure the true progression of the processes studied. To address this challenge, we present GeneTrajectory, an approach that identifies trajectories of genes rather than trajectories of cells. Specifically, optimal transport distances are calculated between gene distributions across the cell-cell graph to extract gene programs and define their gene pseudotemporal order. Here we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation. Moreover, we show that GeneTrajectory deconvolves key gene programs underlying mouse skin hair follicle dermal condensate differentiation that could not be resolved by cell trajectory approaches. GeneTrajectory facilitates the discovery of gene programs that control the changes and activities of biological processes.

Computing hematopoiesis plasticity in response to genetic mutations and environmental stimulations

Cell plasticity (CP), describing a dynamic cell state, plays a crucial role in maintaining homeostasis during organ morphogenesis, regeneration, and trauma-to-repair biological process. Single-cell-omics datasets provide an unprecedented resource to empower CP analysis. Hematopoiesis offers fertile opportunities to develop quantitative methods for understanding CP. In this study, we generated high-quality lineage-negative single-cell RNA-sequencing datasets under various conditions and introduced a working pipeline named scPlasticity to interrogate naïve and disturbed plasticity of hematopoietic stem and progenitor cells with mutational or environmental challenges. Using embedding methods UMAP or FA, a continuum of hematopoietic development is visually observed in wild type where the pipeline confirms a low proportion of hybrid cells ( P hc , with bias range: 0.4∼0.6) on a transition trajectory. Upon Tet2 mutation, a driver of leukemia, or treatment of DSS, an inducer of colitis, P hc is increased and plasticity of hematopoietic stem and progenitor cells was enhanced. We prioritized several transcription factors and signaling pathways, which are responsible for P hc alterations. In silico perturbation suggests knocking out EGR regulons or pathways of IL-1R1 and β-adrenoreceptor partially reverses P hc promoted by Tet2 mutation and inflammation.

Multiplexing cortical brain organoids for the longitudinal dissection of developmental traits at single-cell resolution

Dissecting human neurobiology at high resolution and with mechanistic precision requires a major leap in scalability, given the need for experimental designs that include multiple individuals and, prospectively, population cohorts. To lay the foundation for this, we have developed and benchmarked complementary strategies to multiplex brain organoids by pooling cells from different pluripotent stem cell (PSC) lines either during organoid generation (mosaic models) or before single-cell RNA sequencing (scRNA-seq) library preparation (downstream multiplexing). We have also developed a new computational method, SCanSNP, and a consensus call to deconvolve cell identities, overcoming current criticalities in doublets and low-quality cell identification. We validated both multiplexing methods for charting neurodevelopmental trajectories at high resolution, thus linking specific individuals' trajectories to genetic variation. Finally, we modeled their scalability across different multiplexing combinations and showed that mosaic organoids represent an enabling method for high-throughput settings. Together, this multiplexing suite of experimental and computational methods provides a highly scalable resource for brain disease and neurodiversity modeling.

Towards the Next Generation of Data-Driven Therapeutics Using Spatially Resolved Single-Cell Technologies and Generative AI

Recent advances in multi-omics and spatially resolved single-cell technologies have revolutionised our ability to profile millions of cellular states, offering unprecedented opportunities to understand the complex molecular landscapes of human tissues in both health and disease. These developments hold immense potential for precision medicine, particularly in the rational design of novel therapeutics for treating inflammatory and autoimmune diseases. However, the vast, high-dimensional data generated by these technologies present significant analytical challenges, such as distinguishing technical variation from biological variation or defining relevant questions that leverage the added spatial dimension to improve our understanding of tissue organisation. Generative artificial intelligence (AI), specifically variational autoencoder- or transformer-based latent variable models, provides a powerful and flexible approach to addressing these challenges. These models make inferences about a cell's intrinsic state by effectively identifying complex patterns, reducing data dimensionality and modelling the biological variability in single-cell datasets. This review explores the current landscape of single-cell and spatial multi-omics technologies, the application of generative AI in data analysis and modelling and their transformative impact on our understanding of autoimmune diseases. By combining spatial and single-cell data with advanced AI methodologies, we highlight novel insights into the pathogenesis of autoimmune disorders and outline future directions for leveraging these technologies to achieve the goal of AI-powered personalised medicine.

Oncolytic reprogramming of tumor microenvironment shapes CD4 T-cell memory via the IL6ra-Bcl6 axis for targeted control of glioblastoma

Oncolytic viruses (OVs) emerge as a promising cancer immunotherapy. However, the temporal impact on tumor cells and the tumor microenvironment, and the nature of anti-tumor immunity post-therapy remain largely unclear. Here we report that CD4+ T cells are required for durable tumor control in syngeneic murine models of glioblastoma multiforme after treatment with an oncolytic herpes simplex virus (oHSV) engineered to express IL-12. The upregulated MHCII on residual tumor cells facilitates programmed polyfunctional CD4+ T cells for tumor control and for recall responses. Mechanistically, the proper ratio of Bcl-6 to T-bet in CD4+ T cells navigates their enhanced anti-tumor capacity, and a reciprocal IL6ra-Bcl-6 regulatory axis in a memory CD4+ T-cell subset, which requires MHCII signals from reprogrammed tumor cells, tumor-infiltrating and resident myeloid cells, is necessary for the prolonged response. These findings uncover an OV-induced tumor/myeloid-CD4+ T-cell partnership, leading to long-term anti-tumor immune memory, and improved OV therapeutic efficacy.