Designing RNA sequencing experiments: A practical guide to reproducible gene expression analysis

RNA sequencing (RNA-seq) has become a cornerstone of modern biotechnology, offering a comprehensive and high-resolution view of gene expression that enables the discovery of novel transcripts across diverse biological systems. Its applications extend beyond basic transcriptomics, providing powerful tools for uncovering molecular mechanisms underlying disease, environmental responses, and chemical toxicity. In biotechnology and biomedical research, RNA-seq facilitates the identification of regulatory networks and biomarkers that inform therapeutic development, risk assessment, and precision medicine.

Specific inulin‐type fructan fibers protect against autoimmune diabetes by modulating gut immunity, barrier function, and microbiota homeostasis

Scope Dietary fibers capable of modifying gut barrier and microbiota homeostasis affect the progression of type 1 diabetes (T1D). Here, we aim to compare modulatory effects of inulin‐type fructans (ITFs), natural soluble dietary fibers with different degrees of fermentability from chicory root, on T1D development in nonobese diabetic mice. Methods and results Female nonobese diabetic mice were weaned to long‐ and short‐chain ITFs [ITF(l) and ITF(s), 5%] supplemented diet up to 24 weeks. T1D incidence, pancreatic‐gut immune responses, gut barrier function, and microbiota composition were analyzed. ITF(l) but not ITF(s) supplementation dampened the incidence of T1D. ITF(l) promoted modulatory T‐cell responses, as evidenced by increased CD25 + Foxp3 + CD4 + regulatory T cells, decreased IL17A + CD4 + Th17 cells, and modulated cytokine production profile in the pancreas, spleen, and colon. Furthermore, ITF(l) suppressed NOD like receptor protein 3 caspase‐1‐p20‐IL‐1β inflammasome in the colon. Expression of barrier reinforcing tight junction proteins occludin and claudin‐2, antimicrobial peptides β‐defensin‐1, and cathelicidin‐related antimicrobial peptide as well as short‐chain fatty acid production were enhanced by ITF(l). Next‐generation sequencing analysis revealed that ITF(l) enhanced Firmicutes/Bacteroidetes ratio to an antidiabetogenic balance and enriched modulatory Ruminococcaceae and Lactobacilli . Conclusion Our data demonstrate that ITF(l) but not ITF(s) delays the development of T1D via modulation of gut‐pancreatic immunity, barrier function, and microbiota homeostasis.

Comparing phenotypic manifolds with Kompot: Detecting differential abundance and gene expression at single-cell resolution

Single-cell studies are frequently designed to compare across conditions such as health and disease. However, existing computational approaches typically rely on grouping cells into discrete populations before making comparisons, which can limit resolution for detecting state-dependent changes. Here, we introduce Kompot, a statistical framework for comparative analysis of multi-condition single-cell data. Kompot quantifies both differential abundance, capturing how cells redistribute across the phenotypic space, and differential expression, identifying condition-specific transcriptional changes that may be localized, heterogeneous, or oppositely regulated across states. By modeling cell density and gene expression as continuous functions over a shared cell-state representation, Kompot enables single-cell–resolution inference with principled uncertainty estimates, without requiring predefined clusters or cell types. Applying Kompot to aging murine bone marrow, we identified a continuum of shifts in hematopoietic stem cell and mature cell states, transcriptional remodeling of monocytes independent of compositional changes, and divergent regulation of oxidative stress response genes across cell types. We demonstrate the utility of Kompot in disease settings by identifying cell-state and gene expression changes associated with improved efficacy of combinatorial immunotherapy in melanoma. Additionally, Kompot enables multi-sample comparative analysis by accounting for sample-to-sample heterogeneity. By capturing both global and cell-state–specific effects of perturbation, the Kompot framework is broadly applicable to dissecting condition-specific effects in complex single-cell landscapes. ### Competing Interest Statement The authors have declared no competing interest. National Institutes of Health, R35GM147125, R01CA292932, T32GM136534, S10OD028685 The Mark Foundation for Cancer Research, https://ror.org/00v7th354, Endeavor Award Edward P. Evans Foundation, https://ror.org/03h22gm35, Discovery Research Grant Brotman Baty Institute, https://ror.org/03jxvbk42, Pilot Award

Hierarchical classification of immune cell transcriptomes at population-scale

Accurate immune cell classification is essential for interpreting single-cell RNA sequencing (scRNA-seq) data. However, progress is constrained by the lack of independent, high-resolution benchmarks, as the routine integration of datasets introduces statistical dependencies that artificially inflate model generalizability. Here, we present the single-cell universal classification omnibus (Suco), a resource of independent, uniform expert annotations, and Compocyte, a modular hierarchical classifier. Together, they establish a framework designed for the scale of human population immunology. This approach substantially outperforms existing classifiers while facilitating expert review of ambiguous annotations. Applying Compocyte across 50 studies, including three newly generated datasets, we classified 15.6 million leukocytes from 3,965 patients. Within this expansive cohort, we identified a new tumor-associated resorptive macrophage phenotype, a non-canonical monocyte subtype in subclinical cytokine release syndrome, and the programmatic erosion of T cell memory stemness across metastatic sites. Suco and Compocyte thus provide a generalizable architecture and benchmark capable of sustaining high-resolution annotation across massive clinical cohorts. ### Competing Interest Statement CMR has consulted regarding oncology drug development with Amgen, AstraZeneca, Daiichi Sankyo, Genentech, Merck, and Novartis, and has received licensing and royalty payments for DLL3-directed therapeutics. T.W. reports stock ownership for Roche, Astra Zeneca, Bayer, Innate Pharma, Kyntra, Illumina, 10x Genomics, and Merck KGaA as well as research funding from Atrandi Biosciences, Vilnius, Lithuania; CanVirex AG, Basel Switzerland; and Institut fuer Klinische Krebsforschung GmbH, Frankfurt, Germany, and travel funding from Roche, Basel, Switzerland. S.Z. reports advisory board membership and honoraria from Amgen, Astellas, AstraZeneca, Bayer, Bristol-Myers Squibb, Daiichi Sankyo, Eisai, EUSA, Gilead, Ipsen, Johnson&Johnson, Lilly, MedSir, Medtoday, Merck, MSD, Novartis, Pfizer, Roche, Sanofi Aventis, StreamedUp, Urotrials, Urotube, Zentiva and resarch funding from Eisai. S.Z. reports clinical trial support from Amgen, AstraZeneca, AVEO, Bayer, Biontech, Bristol-Myers Squibb, Calithera, Exelixis, Gilead, Lilly, MSD, Novartis, Pfizer, Roche, Seagen/Astellas, Urotrials and travels & conference support from Amgen, Astellas, AstraZeneca, Bayer, EISAI, Ipsen, Johnson&Johnson, Merck, MSD, Pfizer. All remaining authors declare no relevant competing interests. Spanish Association Against Cancer, PI049999 Federal Ministry of Research, Technology and Space, 001001KT2322 National Cancer Institute, R35 CA263816 National Cancer Institute, U24 CA213274 National Cancer Institute, P30 CA008748 Research Council of Lithuania, P-MIP-24-93

Rectangle: robust and scalable multiscale deconvolution informed by single-cell RNA sequencing data

Bulk RNA-seq enables effective profiling of large cohorts and complex experimental designs, but current single-cell-informed deconvolution methods incompletely resolve closely related cell phenotypes, do not scale efficiently to large single-cell datasets, or fail to account for cellular content not represented in the reference. Here, we present Rectangle, an scverse Python framework for single-cell-informed deconvolution of bulk RNA-seq data.

BMP–Smad1/9 signaling plays a critical role in regulating zebrafish PGC proliferation

The germ cell fate in zebrafish is determined by germ plasm, whereas mammalian germ cell fate is induced by bone morphogenetic protein (BMP) signaling. It remains elusive whether BMP signaling is implicated in zebrafish germ cell development. Here, we demonstrate that BMP–Smad1/9 signaling plays a critical role in zebrafish primordial germ cell (PGC) maintenance rather than fate determination. BMP inhibition or smad1/9 knockdown reduces PGC numbers. Furthermore, we generated PGC-specific smad1/9 knockouts using a transgenic approach with PGC-specifically expressed Cas9 and ubiquitously expressed guide RNAs. Smad1/9 deficiency in PGCs leads to impaired PGC proliferation and increased apoptosis, consequently reducing PGC numbers. Transcriptome analysis revealed unchanged PGC-specific gene expression, but a marked upregulation of DNA damage response-related genes, which is validated by ectopic ATR–pChk1 activation in PGCs and PGC restoring by ATR inhibition. Collectively, these findings underscore conserved but functionally distinct roles of BMP signaling in vertebrate PGC development.

Patient-derived model capturing hypoxia and extracellular matrix remodelling of immunologically cold high-grade serous tumours

High-grade serous carcinoma tumours present poor survival rates, often associated with immunologically excluded environments driven by hypoxia and extensive extracellular matrix remodelling that disrupt tumour-stromal-immune interactions. Current experimental models fail to fully capture these microenvironmental features, limiting understanding of tumour-immune dynamics and drug development. Here, we present bioengineered patient-derived tumour-immune models to mimic physiologically relevant oxygen levels and extracellular matrix remodelling. Cancer cells are co-cultured with cancer-associated fibroblasts within human plasma-3D matrices or grown on decellularized human ovaries. Immune cells are either included within the 3D constructs to study multi-cellular interactions or challenged to infiltrate the matrices. We demonstrate that intratumoural hypoxia acts as a friend and a foe enhancing the activation and cytotoxicity of CD8 + T cells while inducing stromal/matrix dysregulation associated with impaired immune infiltration. Targeting TGF-β signalling attenuates the hypoxia-driven stromal-mediated immune exclusion. These relevant models may aid the development of targeted therapies to transform immunologically cold tumours into immunogenic to benefit female patients.

Pancreatic cancer mRNA vaccine shows lasting results in an early trial

Scientists caution that more research is needed, but nearly all of the patients who responded to the personalized vaccine are still alive six years later.

Dynamic Landscape Analysis of cell fate decisions provides predictive models of neural development from single-cell data

Building a mechanistic understanding of cell fate decisions remains a fundamental goal of developmental biology, with implications for stem cell therapies, regenerative medicine and understanding disease mechanisms. Single-cell transcriptomics provides a detailed picture of the cellular states observed during these decisions, but building dynamic and predictive models from these data remains a challenge. Here, we present dynamic landscape analysis (DLA), an integrative framework that applies dynamical systems theory to identify stable cell states, map transition pathways, and generate a predictive cell fate decision landscape from single-cell data. Applying this framework to vertebrate neural tube development revealed that progenitor specification by Sonic Hedgehog (Shh) can be captured in a landscape with an unexpected topology in which initially divergent lineages converge to the same fate through multiple distinct routes. The model accurately predicted cellular responses and cell fate allocation for unseen dynamic signalling regimes. Cross-species validation using human embryonic organoid data demonstrated conservation of this decision-making architecture. By modelling the dynamic responses that drive cell fate decisions, the DLA framework provides a quantitative and generative framework for extracting mechanistic insights from high-dimensional single-cell data.

Osteoblast-Osteoclast Communication and Bone Homeostasis

Bone remodeling is tightly regulated by a cross-talk between bone-forming osteoblasts and bone-resorbing osteoclasts. Osteoblasts and osteoclasts communicate with each other to regulate cellular behavior, survival and differentiation through direct cell-to-cell contact or through secretory proteins. A direct interaction between osteoblasts and osteoclasts allows bidirectional transduction of activation signals through EFNB2-EPHB4, FASL-FAS or SEMA3A-NRP1, regulating differentiation and survival of osteoblasts or osteoclasts. Alternatively, osteoblasts produce a range of different secretory molecules, including M-CSF, RANKL/OPG, WNT5A, and WNT16, that promote or suppress osteoclast differentiation and development. Osteoclasts also influence osteoblast formation and differentiation through secretion of soluble factors, including S1P, SEMA4D, CTHRC1 and C3. Here we review the current knowledge regarding membrane bound- and soluble factors governing cross-talk between osteoblasts and osteoclasts.

Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks

A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here, we propose the use of conditional single-cell generative adversarial neural networks (cscGAN) for the realistic generation of single-cell RNA-seq data. cscGAN learns non-linear gene–gene dependencies from complex, multiple cell type samples and uses this information to generate realistic cells of defined types. Augmenting sparse cell populations with cscGAN generated cells improves downstream analyses such as the detection of marker genes, the robustness and reliability of classifiers, the assessment of novel analysis algorithms, and might reduce the number of animal experiments and costs in consequence. cscGAN outperforms existing methods for single-cell RNA-seq data generation in quality and hold great promise for the realistic generation and augmentation of other biomedical data types.

Realistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks

A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here we propose the use of conditional single cell Generative Adversarial Neural Networks (cscGANs) for the realistic generation of single cell RNA-seq data. cscGANs learn non-linear gene-gene dependencies from complex, multi cell type samples and use this information to generate realistic cells of defined types. Augmenting sparse cell populations with cscGAN generated cells improves downstream analyses such as the detection of marker genes, the robustness and reliability of classifiers, the assessment of novel analysis algorithms, and might reduce the number of animal experiments and costs in consequence. cscGANs outperform existing methods for single cell RNA-seq data generation in quality and hold great promise for the realistic generation and augmentation of other biomedical data types. *

AI model detects very early normally ‘invisible’ tissue changes of pancreatic cancer

An AI model (REDMOD) can pick up the very early subtle tissue changes of pancreatic ductal adenocarcinoma, the most common form of pancreatic cancer, which conventional imaging and the human eye find difficult to detect, finds research published online in the journal Gut.  As such, it offers the potential to shift an all too common late stage, terminal disease diagnosis to one that is at an early stage (stage 0) and treatable, say the researchers.  While REDMOD was more accurate than experienced radiologists, it requires testing in high risk patients, defined as those with unexpected weight loss and newly diagnosed diabetes, before it can be widely used in clinical practice, they add.

Single-Nucleus Multi-Omic Atlas Maps Regulatory Architecture and Non-Coding Variant Effects across Adult Human Tissues

Diverse human cell types establish specialized functions through lineage- and context-specific regulatory programs. Interpreting non-coding genetic risk requires integrated multi-omic reference maps that directly connect regulatory DNA to cellular expression across human tissues.

ResolVI: addressing noise and bias in spatial transcriptomics

Technologies for estimating RNA expression at high throughput, in intact tissue slices and with high spatial resolution (spatial transcriptomics) shed new light on how cells communicate and tissues function. A fundamental step in analyzing data generated by subcellular resolution spatial transcriptomics technologies is quantification, namely, segmenting the plane into regions, each approximating a cell, and then collating the molecules inside each region to estimate the cellular expression profile. Despite many advances in this area, a persistent problem is that of the incorrect assignment of molecules to cells, which limits many current applications to the level of a priori-defined cell subsets and complicates the discovery of novel cell states. Here we develop resolVI, a model that operates downstream of any segmentation algorithm to generate a probabilistic representation, correcting for the misassignment of molecules, as well as for batch effects and other nuisance factors. We demonstrate that resolVI improves our ability to distinguish between cell states, identify subtle expression changes in space and perform integrated analysis across datasets. ResolVI is available as open source software within scvi-tools.

Metabolites produced by commensal bacteria promote peripheral regulatory T-cell generation

In mice, provision of butyrate—a short-chain fatty acid produced by commensal microorganisms during starch fermentation—facilitates extrathymic generation and differentiation of Foxp3+ regulatory T cells, demonstrating that metabolic by-products are sensed by cells of the immune system and affect the balance between pro- and anti-inflammatory cells.