Single-cell analysis of treatment-resistant prostate cancer: Implications of cell state changes for cell surface antigen-targeted therapies.

Targeting cell surface molecules using radioligand and antibody-based therapies has yielded considerable success across cancers. However, it remains unclear how the expression of putative lineage markers, particularly cell surface molecules, varies in the process of lineage plasticity, wherein tumor cells alter their identity and acquire new oncogenic properties. A notable example of lineage plasticity is the transformation of prostate adenocarcinoma (PRAD) to neuroendocrine prostate cancer (NEPC)-a growing resistance mechanism that results in the loss of responsiveness to androgen blockade and portends dismal patient survival. To understand how lineage markers vary across the evolution of lineage plasticity in prostate cancer, we applied single-cell analyses to 21 human prostate tumor biopsies and two genetically engineered mouse models, together with tissue microarray analysis on 131 tumor samples. Not only did we observe a higher degree of phenotypic heterogeneity in castrate-resistant PRAD and NEPC than previously anticipated but also found that the expression of molecules targeted therapeutically, namely PSMA, STEAP1, STEAP2, TROP2, CEACAM5, and DLL3, varied within a subset of gene-regulatory networks (GRNs). We also noted that NEPC and small cell lung cancer subtypes shared a set of GRNs, indicative of conserved biologic pathways that may be exploited therapeutically across tumor types. While this extreme level of transcriptional heterogeneity, particularly in cell surface marker expression, may mitigate the durability of clinical responses to current and future antigen-directed therapies, its delineation may yield signatures for patient selection in clinical trials, potentially across distinct cancer types.

A developmental constraint model of cancer cell states and tumor heterogeneity.

Cancer is a disease that stems from a fundamental liability inherent to multicellular life forms in which an individual cell is capable of reneging on the interests of the collective organism. Although cancer is commonly described as an evolutionary process, a less appreciated aspect of tumorigenesis may be the constraints imposed by the organism's developmental programs. Recent work from single-cell transcriptomic analyses across a range of cancer types has revealed the recurrence, plasticity, and co-option of distinct cellular states among cancer cell populations. Here, we note that across diverse cancer types, the observed cell states are proximate within the developmental hierarchy of the cell of origin. We thus posit a model by which cancer cell states are directly constrained by the organism's "developmental map." According to this model, a population of cancer cells traverses the developmental map, thereby generating a heterogeneous set of states whose interactions underpin emergent tumor behavior.

Novel cell states arise in embryonic cells devoid of key reprogramming factors.

The capacity for embryonic cells to differentiate relies on a large-scale reprogramming of the oocyte and sperm nucleus into a transient totipotent state. In zebrafish, this reprogramming step is achieved by the pioneer factors Nanog, Pou5f3, and Sox19b (NPS). Yet, it remains unclear whether cells lacking this reprogramming step are directed towards wild type states or towards novel developmental canals in the Waddington landscape of embryonic development. Here we investigate the developmental fate of embryonic cells mutant for NPS by analyzing their single-cell gene expression profiles. We find that cells lacking the first developmental reprogramming steps can acquire distinct cell states. These states are manifested by gene expression modules that result from a failure of nuclear reprogramming, the persistence of the maternal program, and the activation of somatic compensatory programs. As a result, most mutant cells follow new developmental canals and acquire new mixed cell states in development. In contrast, a group of mutant cells acquire primordial germ cell-like states, suggesting that NPS-dependent reprogramming is dispensable for these cell states. Together, these results demonstrate that developmental reprogramming after fertilization is required to differentiate most canonical developmental programs, and loss of the transient totipotent state canalizes embryonic cells into new developmental states in vivo.

Early-stage lung cancer is driven by a transitional cell state dependent on a KRAS-ITGA3-SRC axis.

Glycine-12 mutations in the GTPase KRAS (KRASG12) are an initiating event for development of lung adenocarcinoma (LUAD). KRASG12 mutations promote cell-intrinsic rewiring of alveolar type-II progenitor (AT2) cells, but to what extent such changes interplay with lung homeostasis and cell fate pathways is unclear. Here, we generated single-cell RNA-seq (scRNA-seq) profiles from AT2-mesenchyme organoid co-cultures, mice, and stage-IA LUAD patients, identifying conserved regulators of AT2 transcriptional dynamics and defining the impact of KRASG12D mutation with temporal resolution. In AT2WT organoids, we found a transient injury/plasticity state preceding AT2 self-renewal and AT1 differentiation. Early-stage AT2KRAS cells exhibited perturbed gene expression dynamics, most notably retention of the injury/plasticity state. The injury state in AT2KRAS cells of patients, mice, and organoids was distinguishable from AT2WT states via altered receptor expression, including co-expression of ITGA3 and SRC. The combination of clinically relevant KRASG12D and SRC inhibitors impaired AT2KRAS organoid growth. Together, our data show that an injury/plasticity state essential for lung repair is co-opted during AT2 self-renewal and LUAD initiation, suggesting that early-stage LUAD may be susceptible to interventions that target specifically the oncogenic nature of this cell state.

Challenges in IBD Research 2024: Preclinical Human IBD Mechanisms.

Preclinical human inflammatory bowel disease (IBD) mechanisms is one of 5 focus areas of the Challenges in IBD Research 2024 document, which also includes environmental triggers, novel technologies, precision medicine, and pragmatic clinical research. Herein, we provide a comprehensive overview of current gaps in inflammatory bowel diseases research that relate to preclinical research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in IBD interception, remission, and restoration. The document is the result of multidisciplinary input from scientists, clinicians, patients, and funders and represents a valuable resource for patient-centric research prioritization. This preclinical human IBD mechanisms section identifies major research gaps whose investigation will elucidate pathways and mechanisms that can be targeted to address unmet medical needs in IBD. Research gaps were identified in the following areas: genetics, risk alleles, and epigenetics; the microbiome; cell states and interactions; barrier function; IBD complications (specifically fibrosis and stricturing); and extraintestinal manifestations. To address these gaps, we share specific opportunities for investigation for basic and translational scientists and identify priority actions.

To address the unmet medical needs of patients with inflammatory bowel diseases (IBD) and move toward cures, preclinical human-relevant research must center on mechanistic questions pertinent to patients with IBD in the 3 areas of disease interception, remission, and restoration.

Control of cell state transitions by post-transcriptional regulation.

Cell state transitions are prevalent in biology, playing a fundamental role in development, homeostasis and repair. Dysregulation of cell state transitions can lead to or occur in a wide range of diseases. In this letter, I explore and highlight the role of post-transcriptional regulatory mechanisms in determining the dynamics of cell state transitions. I propose that regulation of protein levels after transcription provides an under-appreciated regulatory route to obtain fast and sharp transitions between distinct cell states. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Multi-omics analysis of innate and adaptive responses to BCG vaccination reveals epigenetic cell states that predict trained immunity.

Immune responses are tightly regulated yet highly variable between individuals. To investigate human population variation of trained immunity, we immunized healthy individuals with Bacillus Calmette-Guérin (BCG). This live-attenuated vaccine induces not only an adaptive immune response against tuberculosis but also triggers innate immune activation and memory that are indicative of trained immunity. We established personal immune profiles and chromatin accessibility maps over a 90-day time course of BCG vaccination in 323 individuals. Our analysis uncovered genetic and epigenetic predictors of baseline immunity and immune response. BCG vaccination enhanced the innate immune response specifically in individuals with a dormant immune state at baseline, rather than providing a general boost of innate immunity. This study advances our understanding of BCG's heterologous immune-stimulatory effects and trained immunity in humans. Furthermore, it highlights the value of epigenetic cell states for connecting immune function with genotype and the environment.

Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS.

Reproducible definition and identification of cell types is essential to enable investigations into their biological function, and understanding their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here, we propose an unsupervised method, MMIDAS, which combines a generalized mixture model with a multi-armed deep neural network, to jointly infer the discrete type and continuous type-specific variability. Using four recent datasets of brain cells spanning different technologies, species, and conditions, we demonstrate that MMIDAS can identify reproducible cell types and infer cell type-dependent continuous variability in both uni-modal and multi-modal datasets.

Single-cell transcriptional profiling reveals aberrant gene expression patterns and cell states in autoimmune diseases.

Multiple sclerosis(MS), primary Sjögren syndrome (pSS), and systemic lupus erythematosus (SLE) share numerous clinical symptoms and serological characteristics. We analyzed 153550 cells of scRNA-seq data of 17 treatment-naive patients (5 MS, 5 pSS, and 7 SLE) and 10 healthy controls, and we examined the enrichment of biological processes, differentially expressed genes (DEGs), immune cell types, and their subpopulations, and cell-cell communication in peripheral blood mononuclear cells (PBMCs). The percentage of B cells, megakaryocytes, monocytes, and proliferating T cells presented significant changes in autoimmune diseases. The enrichment of cell types based on gene expression revealed an elevated monocyte. MIF, MK, and GALECTIN signaling networks were obvious differences in autoimmune diseases. Taken together, our analysis provides a comprehensive map of the cell types and states of ADs patients at the single-cell level to understand better the pathogenesis and treatment of these ADs.

Dissecting cellular states of infiltrating microenvironment cells in melanoma by integrating single-cell and bulk transcriptome analysis.

BACKGROUND: Cellular states of different immune cells can affect the activity of the whole immune microenvironment. METHODS: Here, leveraging reference profiles of microenvironment cell states that were constructed based on single-cell RNA-seq data of melanoma, we dissected the composition of microenvironment cell states across 463 skin cutaneous melanoma (SKCM) bulk samples through CIBERSORT-based deconvolution of gene expression profiles and revealed high heterogeneity of their distribution. Correspondence analysis on the estimated cellular fractions of melanoma bulk samples was performed to identify immune phenotypes. Based on the publicly available clinical survival and therapy data, we analyzed the relationship between immune phenotypes and clinical outcomes of melanoma. RESULTS: By analysis of the relationships among those cell states, we further identified three distinct tumor microenvironment immune phenotypes: "immune hot/active", "immune cold-suppressive" and "immune cold-exhausted". They were characterized by markedly different patterns of cell states: most notably the CD8 T Cytotoxic state, CD8 T Mixed state, B non-regulatory state and cancer-associated fibroblasts (CAFs), depicting distinct types of antitumor immune response (or immune activity). These phenotypes had prognostic significance for progression-free survival and implications in response to immune therapy in an independent cohort of anti-PD1 treated melanoma patients. CONCLUSIONS: The proposed strategy of leveraging single-cell data to dissect the composition of microenvironment cell states in individual bulk tumors can also extend to other cancer types, and our results highlight the importance of microenvironment cell states for the understanding of tumor immunity.

Alveolar type I cells can give rise to KRAS-induced lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most prevalent subtype of lung cancer and presents clinically with a high degree of biological heterogeneity and distinct clinical outcomes. The current paradigm of LUAD etiology posits alveolar epithelial type II (AT2) cells as the primary cell of origin, while the role of AT1 cells in LUAD oncogenesis remains unknown. Here, we examine oncogenic transformation in mouse Gram-domain containing 2 (Gramd2)+ AT1 cells via oncogenic KRASG12D. Activation of KRASG12D in AT1 cells induces multifocal LUAD, primarily of papillary histology. Furthermore, KRT8+ intermediate cell states were observed in both AT2- and AT1-derived LUAD, but SCGB3A2+, another intermediate cell marker, was primarily associated with AT1 cells, suggesting different mechanisms of tumor evolution. Collectively, our study reveals that Gramd2+ AT1 cells can serve as a cell of origin for LUAD and suggests that distinct subtypes of LUAD based on cell of origin be considered in the development of therapeutics.

Human microglial state dynamics in Alzheimer's disease progression.

Altered microglial states affect neuroinflammation, neurodegeneration, and disease but remain poorly understood. Here, we report 194,000 single-nucleus microglial transcriptomes and epigenomes across 443 human subjects and diverse Alzheimer's disease (AD) pathological phenotypes. We annotate 12 microglial transcriptional states, including AD-dysregulated homeostatic, inflammatory, and lipid-processing states. We identify 1,542 AD-differentially-expressed genes, including both microglia-state-specific and disease-stage-specific alterations. By integrating epigenomic, transcriptomic, and motif information, we infer upstream regulators of microglial cell states, gene-regulatory networks, enhancer-gene links, and transcription-factor-driven microglial state transitions. We demonstrate that ectopic expression of our predicted homeostatic-state activators induces homeostatic features in human iPSC-derived microglia-like cells, while inhibiting activators of inflammation can block inflammatory progression. Lastly, we pinpoint the expression of AD-risk genes in microglial states and differential expression of AD-risk genes and their regulators during AD progression. Overall, we provide insights underlying microglial states, including state-specific and AD-stage-specific microglial alterations at unprecedented resolution.

Multimodal characterization of murine gastruloid development.

Gastruloids are 3D structures generated from pluripotent stem cells recapitulating fundamental principles of embryonic pattern formation. Using single-cell genomic analysis, we provide a resource mapping cell states and types during gastruloid development and compare them with the in vivo embryo. We developed a high-throughput handling and imaging pipeline to spatially monitor symmetry breaking during gastruloid development and report an early spatial variability in pluripotency determining a binary response to Wnt activation. Although cells in the gastruloid-core revert to pluripotency, peripheral cells become primitive streak-like. These two populations subsequently break radial symmetry and initiate axial elongation. By performing a compound screen, perturbing thousands of gastruloids, we derive a phenotypic landscape and infer networks of genetic interactions. Finally, using a dual Wnt modulation, we improve the formation of anterior structures in the existing gastruloid model. This work provides a resource to understand how gastruloids develop and generate complex patterns in vitro.

scEvoNet: a gradient boosting-based method for prediction of cell state evolution.

BACKGROUND: Exploring the function or the developmental history of cells in various organisms provides insights into a given cell type's core molecular characteristics and putative evolutionary mechanisms. Numerous computational methods now exist for analyzing single-cell data and identifying cell states. These methods mostly rely on the expression of genes considered as markers for a given cell state. Yet, there is a lack of scRNA-seq computational tools to study the evolution of cell states, particularly how cell states change their molecular profiles. This can include novel gene activation or the novel deployment of programs already existing in other cell types, known as co-option. RESULTS: Here we present scEvoNet, a Python tool for predicting cell type evolution in cross-species or cancer-related scRNA-seq datasets. ScEvoNet builds the confusion matrix of cell states and a bipartite network connecting genes and cell states. It allows a user to obtain a set of genes shared by the characteristic signature of two cell states even between distantly-related datasets. These genes can be used as indicators of either evolutionary divergence or co-option occurring during organism or tumor evolution. Our results on cancer and developmental datasets indicate that scEvoNet is a helpful tool for the initial screening of such genes as well as for measuring cell state similarities. CONCLUSION: The scEvoNet package is implemented in Python and is freely available from https://github.com/monsoro/scEvoNet . Utilizing this framework and exploring the continuum of transcriptome states between developmental stages and species will help explain cell state dynamics.

Profiling Cellular Ecosystems at Single-Cell Resolution and at Scale with EcoTyper.

Tissues are composed of diverse cell types and cellular states that organize into distinct ecosystems with specialized functions. EcoTyper is a collection of machine learning tools for the large-scale delineation of cellular ecosystems and their constituent cell states from bulk, single-cell, and spatially resolved gene expression data. In this chapter, we provide a primer on EcoTyper and demonstrate its use for the discovery and recovery of cell states and ecosystems from healthy and diseased tissue specimens.

The Heterogeneous Cellular States of Glioblastoma Stem Cells Revealed by Single Cell Analysis.

Glioblastoma stem cells (GSCs) contributed to the progression, treatment resistance, and relapse of glioblastoma (GBM). However, current researches on GSCs were performed usually outside the human tumor microenvironment, ignoring the importance of the cellular states of primary GSCs. In this study, we leveraged single-cell transcriptome sequencing data of 6 independent GBM cohorts from public databases, and combined lineage and stemness features to identify primary GSCs. We dissected the cell states of GSCs and correlated them with the clinical outcomes of patients. As a result, we constructed a cellular hierarchy where GSCs resided at the center. In addition, we identified and characterized 2 different and recurrent GSCs subpopulations: proliferative GSCs (pGSCs) and quiescent GSCs (qGSCs). The pGSCs showed high cell cycle activity, indicating rapid cell division, while qGSCs showed a quiescent state. Then we traced the processes of tumor development by pseudo-time analysis and tumor phylogeny, and found that GSCs accumulated throughout the whole tumor development period. During the process, pGSCs mainly contributed to the early stage and qGSCs were enriched in the later stage. Finally, we constructed an 8-gene prognostic signature reflecting pGSCs activity and found that patients whose tumors were enriched for the pGSC signature had poor clinical outcomes. Our study highlights the primary GSCs heterogeneity and its correlation to tumor development and clinical outcomes, providing the potential targets for GBM treatment.

Transcriptomic profiling of nuclei from paraformaldehyde-fixed and formalin-fixed paraffin-embedded brain tissues.

BACKGROUND: Paraformaldehyde (PFA) fixation is necessary for histochemical staining, and formalin-fixed and paraffin-embedded (FFPE) tissue archives are the largest repository of clinically annotated specimens. Single-cell gene expression workflows have recently been developed for PFA-fixed and FFPE tissue specimens. However, for tissues where intact cells are hard to recover, including tissues containing highly interconnected neurons, single-nuclear transcriptomics is beneficial. Moreover, since RNA is very unstable, the effects of standard pathological practice on the transcriptome of samples obtained from such archived specimens like FFPE samples are largely anecdotal. RESULTS: We evaluated the effects of polyformaldehyde (PFA) fixation and paraffin-embedding on transcriptional profiles of the mouse hippocampus obtained by RNA sequencing (RNA-seq). The transcriptomic signatures of nuclei isolated from fresh PFA-fixed and fresh FFPE tissues were comparable to those of cryopreserved samples. However, more differentially expressed genes were obtained for brains after PFA fixation for more than 3 days than in fresh PFA-fixed samples, especially genes involved in spliceosome and synaptic-related pathways. Importantly, the real cell states were destroyed, with oligodendrocyte precursor cells depleted in the 1day fixed hippocampus. After fixation for 3 days, the proportions of neuronal cells and oligodendrocytes decreased and microglia increased; however, relative frequencies remained constant for longer fixation durations. The storage time of FFPE samples had a negligible effect on the cell composition. SIGNIFICANCE: This represents the first work to investigate the effects of fixation and storage time of brains on its nuclear transcriptome signatures in detail. The fixation time had more influences on the nuclear transcriptomic profiles than FFPE retention time, and the cliff-like effects appeared to occur over a fixed period of 1-3 days. These findings are expected to guide sample preparation for single-nucleus RNA-seq of FFPE samples, particularly in transcriptomic studies focused on brain diseases.

Heterogeneous levels of delta-like 4 within a multinucleated niche cell maintains muscle stem cell diversity.

The quiescent muscle stem cell (QSC) pool is heterogeneous and generally characterized by the presence and levels of intrinsic myogenic transcription factors. Whether extrinsic factors maintain the diversity of states across the QSC pool remains unknown. The muscle fiber is a multinucleated syncytium that serves as a niche to QSCs, raising the possibility that the muscle fiber regulates the diversity of states across the QSC pool. Here, we show that the muscle fiber maintains a continuum of quiescent states, through a gradient of Notch ligand, Dll4, produced by the fiber and captured by QSCs. The abundance of Dll4 captured by the QSC correlates with the protein levels of the stem cell (SC) identity marker, Pax7. Niche-specific loss of Dll4 decreases QSC diversity and shifts the continuum to cell states that are biased toward more proliferative and committed fates. We reveal that fiber-derived Mindbomb1 (Mib1), an E3 ubiquitin ligase activates Dll4 and controls the heterogeneous levels of Dll4. In response to injury, with a Dll4-replenished niche, the normal continuum and diversity of the SC pool is restored, demonstrating bidirectionality within the SC continuum. Our data show that a post-translational mechanism controls heterogeneity of Notch ligands in a multinucleated niche cell to maintain a continuum of metastable states within the SC pool during tissue homeostasis.

Turning Data to Knowledge: Online Tools, Databases, and Resources in microRNA Research.

MicroRNAs (miRNAs) provide a fundamental layer of regulation in cells. miRNAs act posttranscriptionally through complementary base-pairing with the 3'-UTR of a target mRNA, leading to mRNA degradation and translation arrest. The likelihood of forming a valid miRNA-target duplex within cells was computationally predicted and experimentally monitored. In human cells, the miRNA profiles determine their identity and physiology. Therefore, alterations in the composition of miRNAs signify many cancer types and chronic diseases. In this chapter, we introduce online functional tools and resources to facilitate miRNA research. We start by introducing currently available miRNA catalogs and miRNA-gateway portals for navigating among different miRNA-centric online resources. We then sketch several realistic challenges that may occur while investigating miRNA regulation in living cells. As a showcase, we demonstrate the utility of miRNAs and mRNAs expression databases that cover diverse human cells and tissues, including resources that report on genetic alterations affecting miRNA expression levels and alteration in binding capacity. Introducing tools linking miRNAs with transcription factor (TF) networks reveals miRNA regulation complexity within living cells. Finally, we concentrate on online resources that analyze miRNAs in human diseases and specifically in cancer. Altogether, we introduce contemporary, selected resources and online tools for studying miRNA regulation in cells and tissues and their utility in health and disease.