Modeling developmental morpho-dynamics at single-cell resolution.

In the last few years, single cell and single nucleus RNA-sequencing technologies have been leveraged in exercised adult skeletal muscle to reveal complex cell type-specific regulation of muscle plasticity. Expanding -omics technology at single cell resolution, complimented by advanced computational approaches, promise to rapidly accelerate our understanding of how exercise confers its numerous beneficial effects, paving the way for cell type-specific and targeted therapeutics to increase exercise responses during aging and in the face of chronic disease.

Background: DNMT3A and TET2 are epigenetic regulator genes commonly mutated in age-related clonal hematopoiesis (CH). Despite having opposed epigenetic functions, these mutations are associated with increased all-cause mortality and a low risk for hematological neoplasms. In this study, we make use of a natural inflammatory response occurring during COVID-19 to define the impact of these mutations on inflammation and outcomes. Methods: A cohort of 243 community-based patients (Olmsted County, MN) with COVID-19 were identified. CH status was determined on PBMC using error corrected sequencing (lower detection limit 0.5% VAF). In a subset of patients, we use multi-omics interrogation of the genome, transcriptome, and epigenome at single cell (sc) resolution along with DNA methylation, in PBMC, from patients with COVID-19 and CH. Results: We detected 97 CH mutations in 72 (29.6%) of 243 patients with COVID-19. The most frequent being DNMT3A (n=30, 30%) and TET2 (n=26, 28%). The presence of CH negatively impacted overall survival when restricted to TET2 and DNMT3A mutations. However, only DNMT3Amt CH retained age and comorbidity independent prognostication (HR 2.84, p = 0.022). We then assessed for changes in DNA methylation using Illumina MethylationEPIC array on patients with COVID-19 and DNMT3A (n=3) or TET2mt (n=4) CH. Site specific DNA methylation changes revealed significant loss of methylation in DNMT3Amt CH in comparison to TET2mt patients, with 10,944 hypomethylated and 1,160 hypermethylated sites. We found that actively transcribed states (Tx, TxWk) were more commonly hypomethylated in DNMT3Amt CH while enhancers (Enh) and promoters (TssA, TssAFlnk) were more commonly hypomethylated in TET2mt CH. We then conducted scDNA-seq in DNMT3Amt (n=3) and TET2mt (n=1) CH in COVID-19 and observed that while in TET2mt CH, mutations were largely restricted to classical and intermediate monocytes, in DNMT3Amt CH, mutations were seen in myeloid and lymphoid lineage cells. We performed scRNA-seq on patients with COVID-19 and DNMT3A (n=3) or TET2mt (n=6) CH. Differential gene expression analysis identified 1,569 upregulated genes in DNMT3Amt patients and 205 downregulated. Pathway analysis demonstrated an upregulation of genes involved in T cell survival, function, and inflammation in DNMT3Amt patients compared to TET2mt patients with a significant overexpression of GIMAP1, GIMAP4, IRF2, IL32 in multiple cell types. We correlated transcriptomic changes with chromatin accessibility using the 10X Genomics Multiome platform to profile both gene expression and open chromatin from the same samples. Analysis of global distribution of cut sites and differentially accessible peaks showed increased chromatin accessibility in DNMT3Amt CH, especially CD4+ T lymphocytes and NK cells. Conclusion: We demonstrate an age, sex, and comorbidity-independent adverse impact on mortality associated with DNMT3Amt CH in the context of COVID-19 and highlight transcriptomic and epigenetic differences between DNMT3A and TET2mt CH, with single cell resolution. Citation Format: Jenna A Fernandez, Wazim Mohammed Ismail, Moritz Binder, Terra Lasho, Susan M. Geyer, Amelia Mazzone, Christy M. Finke, Abhishek Mangaonkar, Jeong-Heon Lee, Liguo Wang, Vernadette A. Simon, Fariborz Rakhshan Rohakthar, Amik Munankarmy, Jonathan J. Harrington, Melissa R. Snyder, Keith D. Robertson, Alexandre Gaspar-Maia, Mrinal Patnaik. Divergent impacts of DNMT3A and TET2 mutant clonal hematopoiesis on COVID-19 related outcomes [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Methylation, Clonal Hematopoiesis, and Cancer; 2025 Feb 1-4; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2025;85(3 Suppl):Abstract nr A012.

BackgroundPediatric high-grade glioma (pHGG) is a deadly childhood cancer, with five-year survival under 20%. Conventional therapy consisting of radiation and, where possible, surgical resection remains the standard of care. Successful...

Capturing the heterogeneous phenotypes of microbial populations at relevant spatiotemporal scales is highly challenging. Here, we present par-seqFISH (parallel sequential fluorescence in situ hybridization), a transcriptome-imaging approach that records gene expression and spatial context within microscale assemblies at a single-cell and molecule resolution. We applied this approach to the opportunistic pathogen Pseudomonas aeruginosa, analyzing about 600,000 individuals across dozens of conditions in planktonic and biofilm cultures. We identified numerous metabolic- and virulence-related transcriptional states that emerged dynamically during planktonic growth, as well as highly spatially resolved metabolic heterogeneity in sessile populations. Our data reveal that distinct physiological states can coexist within the same biofilm just several micrometers away, underscoring the importance of the microenvironment. Our results illustrate the complex dynamics of microbial populations and present a new way of studying them at high resolution.

Metabolism underlies cell growth, survival, and function, yet its activities vary widely across cell types and tissue environments. Spatially resolving these processes in situ at single-cell resolution is essential to advance our understanding of cellular function and tissue physiology in health and disease. However, existing approaches are limited by either destructive workflows, insufficient spatial resolution and biochemical specificity, or lack of direct linkage to cell identity. Here, we present Raman Enhanced Delineation of Cell Atlases in Tissues (REDCAT), a multimodal all-optical platform that integrates stimulated Raman scattering, autofluorescence redox imaging, second harmonic generation, and high-plex immunofluorescence to co-map metabolic activities and cell types within the same tissue section. REDCAT achieves subcellular resolution profiling of protein, lipid, redox, and nuclear acid metabolism, together with extracellular matrix composition, in both FFPE and fresh-frozen human tissues. Applied to normal lymph nodes, REDCAT delineated distinct redox and lipid remodeling programs across germinal center B-cell zones and immune subsets, highlighting cell-type-specific metabolic specialization. In lymphoma, it revealed profound metabolic reprogramming, including extensive lipid accumulation, nuclear metabolic heterogeneity, and a transitional metabolic state associated with transformation from chronic lymphocytic leukemia to diffuse large B-cell lymphoma, thereby illuminating tumor evolution in situ . In human liver, REDCAT resolved cell-type-specific lipid droplet diversity and zonation-dependent nuclear metabolic gradients, uncovering new principles of spatial metabolic organization. By directly linking cell identity with spatial metabolic states at single-cell or subcellular resolution, REDCAT establishes a broadly applicable framework for studying immune function, tumor progression, and tissue physiology, and offers a new path to deciphering the metabolic basis of health and disease.

BackgroundThe tumor microenvironment (TME) is composed of highly heterogeneous cell types that dynamically interact with each other. To more precisely distinguish and characterize cellular diversity and disease states within tissues,...

Drift chambers are a type of gaseous ionization detector used in high-energy physics experiments. They can identify charged particles and measure their momentum. When a high-energy charged particle crosses the drift chamber, it ionizes the gas. The liberated electrons drift towards positive-high-voltage wires where an ionization avalanche amplifies the signal. Traditional drift chambers use only the arrival time of the cluster of charge from the closest ionization for tracking, and use only the integral of the whole signal for particle identification. We constructed prototype drift chambers with the ability to resolve the charge cluster signals from individual ionization events. Different algorithms were studied and optimized to best detect the clusters. The improvements to particle identification were studied using a single-cell prototype detector, while the improvements to particle tracking were studied using a multiple-layer prototype. The prototypes were built in the context of initial work for the now-cancelled SuperB project, but the results apply to any drift chambers used in flavour-factory experiments. The results show that the choice of algorithm is not as critical as properly optimizing the algorithm parameters for the dataset. We find that a smoothing time of a few nanoseconds is optimal. This corresponds to bandwidth of a few hundred megahertz, indicating that gigahertz-bandwidth electronics are not required to make use of this technique. Particle identification performance is quantified by the fraction of real pions correctly identified as pions with at most 10% of real pions mis-identified as muons. In our single-cell prototype, the performance increases from 50% to 60% of pions correctly identified when cluster counting is combined with a traditional truncated-mean charge measurement, compared to the charge measurement alone. Tracking performance is quantified by the single-cell resolution: the uncertainty in measuring the distance of charged particle tracks from a given sense wire. In our multiple-layer prototype, the single-cell tracking resolution using traditional methods is measured to be ~150μm. With cluster counting implemented, the resolution is unchanged, indicating that the additional cluster information is not useful.

Hepatocellular carcinoma (HCC) is the most common liver cancer and arises from the malignant transformation of hepatocytes. HCC tumors often express genes critical for hepatocyte development but the regulatory mechanisms driving aberrant expression remain unclear. Recent evidence indicates that epigenetic alterations contribute to HCC progression with nearly 50% of cases associated with mutations in chromatin modifiers. Thus, it is imperative to understand how chromatin is altered in HCC and identify the DNA regulatory sequences associated with HCC tumorigenesis. Our study aims to map the chromatin landscape and transcriptome in HCC tumors using a multiomic approach at single cell resolution. Single-cell ATAC- and RNA-seq will be conducted in HCC tumors, matched controls and normal livers to identify regulatory sequences and their target genes critical for HCC tumor progression. We will characterize these regulatory sequences in the context of liver development using genetic manipulation and epigenomic datasets generated from human pluripotent stem cells differentiated to hepatic lineages. Our goal is to elucidate how epigenetic alterations in HCC drive cancer progression and how they relate to a dedifferentiated state. Citation Format: Miguel Ramirez, Tabea Stephan, Daivd Schaeffer, Pamela Hoodless. Defining chromatin alterations in liver cancer at single cell resolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4412.

Tumor initiation represents the first step in tumorigenesis during which normal progenitor cells undergo cell fate transition to cancer. Capturing this process as it occurs in vivo, however, remains elusive. Here we employ spatiotemporally controlled oncogene activation and tumor suppressor inhibition together with multiomics to unveil the processes underlying oral epithelial progenitor cell reprogramming into tumor initiating cells at single cell resolution. Tumor initiating cells displayed a distinct stem-like state, defined by aberrant proliferative, hypoxic, squamous differentiation, and partial epithelial to mesenchymal invasive gene programs. YAP-mediated tumor initiating cell programs included activation of oncogenic transcriptional networks and mTOR signaling, and recruitment of myeloid cells to the invasive front contributing to tumor infiltration. Tumor initiating cell transcriptional programs are conserved in human head and neck cancer and associated with poor patient survival. These findings illuminate processes underlying cancer initiation at single cell resolution, and identify candidate targets for early cancer detection and prevention.

While exogenous metal/metalloid (metal) exposure has been associated with reduced human semen quality, no study has assessed the associations of exogenous metals in human spermatozoa with semen quality. Here, we developed a strategy to explore the associations between exogenous metals in spermatozoa at single-cell resolution and human semen quality among 84 men screened as sperm donors, who provided 266 semen samples within 90 days. A cellular atlas of exogenous metals at the single-cell level was created with mass cytometry (CyTOF) technology, which concurrently displayed 18 metals in more than 50 000 single sperm. Exogenous metals in spermatozoa at single-cell resolution were extremely heterogeneous and diverse. Further analysis using multivariable linear regression and linear mixed-effects models revealed that the heterogeneity and prevalence of the exogenous metals at single-cell resolution were associated with semen quality. The heterogeneity of lead (Pb), tin (Sn), yttrium (Y), and zirconium (Zr) was negatively associated with sperm concentration and count, while their prevalence showed positive associations. These findings revealed that the heterogeneous properties of exogenous metals in spermatozoa were associated with human semen quality, highlighting the importance of assessing exogenous metals in spermatozoa at single-cell resolution to evaluate male reproductive health risk precisely.

.SignificanceThree-dimensional (3D) two-photon patterned illumination using a combination of computer-generated holography (CGH) and wide-field temporal focusing (TF) has emerged as a highly effective approach for photostimulation. However, even though the axial full-width at half-maximum of a single-spot by TF is smaller than the single-cell size of , the axial resolution of 3D multispot patterns produced by CGH with TF is lower than the single-cell resolution as a result of interference among multispots.AimWe aim to achieve 3D two-photon patterned illumination with single-cell resolution by combining CGH with time-multiplexed multiline temporal focusing (TM-ML-TF), which is implemented by adding an echelle grating at a position conjugate to the focal plane of the TF-CGH system.ApproachWe measure the 3D two-photon fluorescence distributions generated by the TF-CGH and TM-ML-TF-CGH systems.ResultsIn TM-ML-TF, the crosstalk artifacts between the target spots in two proximal planes with an axial distance of were suppressed from 81% to 15% compared with those in TF. We successfully achieved the photoconversion of 3D target cells in spheroids with single-cell resolution.ConclusionsTM-ML-TF successfully suppresses the interference among multispots, enabling the TM-ML-TF-CGH system to provide precise 3D patterned illumination with single-cell resolution.

Single-cell RNA-seq Reveals Chemoradiotherapy-induced Immune Microenvironment Modulation in Locally Advanced Cervical Cancer

The inference of cell–cell communication (CCC) is crucial for a better understanding of complex cellular dynamics and regulatory mechanisms in biological systems. However, accurately inferring spatial CCCs at single-cell resolution remains a significant challenge. To address this issue, we present a versatile method, called DeepTalk, to infer spatial CCC at single-cell resolution by integrating single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data. DeepTalk utilizes graph attention network (GAT) to integrate scRNA-seq and ST data, which enables accurate cell-type identification for single-cell ST data and deconvolution for spot-based ST data. Then, DeepTalk can capture the connections among cells at multiple levels using subgraph-based GAT, and further achieve spatially resolved CCC inference at single-cell resolution. DeepTalk achieves excellent performance in discovering meaningful spatial CCCs on multiple cross-platform datasets, which demonstrates its superior ability to dissect cellular behavior within intricate biological processes.

The Escherichia coli lacZ gene encoding β-galactosidase is a widely used reporter, but few synthetic substrates are available for detecting its activity with single-cell resolution in living samples. Our recently reported fluorogenic substrate SPiDER-βGal is suitable for this purpose, but its hydrolysis product shows green fluorescence emission, and a red-shifted analogue is therefore required for use in combination with green fluorescent protein (GFP) markers. Herein, we describe the development of a red-shifted fluorogenic substrate for β-galactosidase, SPiDER-Red-βGal, based on a silicon rhodol scaffold and a carboxylic group as the intramolecular nucleophile. LacZ-positive cells were successfully labeled with SPiDER-Red-βGal at single-cell resolution in living samples, which enabled us to visualize different cell types in combination with GFP markers.

Metabolic reprogramming has been defined as a key hall mark of human tumors. However, metabolic heterogeneity in gastric cancer has not been elucidated. Here we separated the TCGA-STAD dataset into two metabolic subtypes. The differences between subtypes were elaborated in terms of transcriptomics, genomics, tumor-infiltrating cells, and single-cell resolution. We found that metabolic subtype 1 is predominantly characterized by low metabolism, high immune cell infiltration. Subtype 2 is mainly characterized by high metabolism and low immune cell infiltration. From single-cell resolution, we found that the high metabolism of subtype 2 is dominated by epithelial cells. Not only epithelial cells, but also various immune cells and stromal cells showed high metabolism in subtype 2 and low metabolism in subtype 1. Our study established a classification of gastric cancer metabolic subtypes and explored the differences between subtypes from multiple dimensions, especially the single-cell resolution.