Association for Single Cell Analysis (ASCA)

📍 A quantitative, time-resolved model of human T cell fate specification   Michaels et al. used genetic perturbations, transcribed lineage barcoding, and advanced computational tools to dissect the gene expression dynamics and cell fate decisions that control T lineage specification from stem cells   Highlights 🔹 Pluripotent stem cells are a resource for studying human immune fate specification 🔹 Knocking out YBX1 reduces T lineage output 🔹 Mast potential and myeloid potential diverge early during blood development 🔹 T cells and myeloid cells have a high degree of shared clonal ancestry ➡️ More details: https://lnkd.in/eDnakKNs #UniversityofBritishColumbia #ubc #spatialomics #spatialbiology #singlecellanalysis #singlecell

📍 The evolutionary dynamics of cancer proliferation in single-cell clones Lucas et al. introduce the SPRINTER algorithm that uses single-cell whole-genome DNA sequencing data to enable accurate identification and clone assignment of S- and G2-phase cells, as assessed by generating accurate ground truth data.   Key findings ✔️ Widespread clone proliferation heterogeneity, orthogonally supported by Ki-67 staining, nuclei imaging and clinical imaging ✔️ High-proliferation clones demonstrating increased metastatic seeding potential, increased circulating tumor DNA shedding and clone-specific altered replication timing ✔️ Increased single-cell rates of different genomic variants and enrichment of proliferation-related gene amplifications in high-proliferation clones ➡️ More details: https://lnkd.in/ePjWa3bh #spatialomics #spatialbiology #singlecellanalysis #singlecell

📍 Systemic coordination of whole-body tissue remodeling during local regeneration in sea anemones Cheung et al. conducted an in-depth investigation of regeneration in the cnidarian Nematostella vectensis, comparing intact and regenerating animals, from genome-wide expression patterns and molecular activities to tissue dynamics and overall organismal shape.    Highlights 📍 Tomo-seq identifies local and systemic responses in regenerating Nematostella 📍 Physa regeneration triggers both morphallactic and epimorphic processes 📍 Local and systemic matrix metalloprotease levels scale with tissue loss 📍 Matrix metalloproteases remodel basement membranes to maintain shape homeostasis ➡️ More details: https://lnkd.in/ee7rQrFA #EMBL #spatialomics #spatialbiology #singlecellanalysis #singlecell

📍 Tissue-of-origin and spatially dependent metabolic rewiring in renal and colorectal cancer Maspero et al. presented Spatial Flux Balance Analysis (spFBA), a novel framework that processes spatial transcriptomics data to enrich metabolic fluxes at the spatial spot resolution.   Key points 🔸 Steady-state modeling and integration of the transcriptional information into flux boundary constraints 🔸 The simulation of fluxes while accounting for indirect transcriptional regulation via substrate availability 🔸 Enhancing the expressive power of traditional pathway enrichment frameworks by capturing reaction directionality and metabolic growth rates 🔸 Revealing distinct metabolically-defined subpopulations with spatial arrangements in renal and colorectal cancer ➡️ More details: https://lnkd.in/e5_iZucJ #UniversityofMilanoBicocca #spatialomics #spatialbiology #singlecellanalysis #singlecell

📍 A sample multiplexing protocol for droplet-based simultaneous single nuclei RNA- and ATAC-seq systems Li and Haydar presented a Multiplexed Multiome (MuMu) protocol that for the first time allows the pooling of single nuclei from multiple biological samples prior to performing simultaneous single nuclei RNA-seq and ATAC-seq.   Key points 💥 Assembling the custom Tn5 transposome 💥 Performing the transposition reaction and nuclei pooling 💥 Sequencing library preparation and data pre-processing 💥 Greatly reducing the cost of sn-Multiome ➡️ More details: https://lnkd.in/eCWjju6B #ChildrensNationalMedicalCenter #spatialomics #spatialbiology #singlecellanalysis #singlecell

📍 Granzyme K+CD8+ T cells interact with fibroblasts to promote neutrophilic inflammation in nasal polyps Guo et al. integrated scRNA-seq, single-cell TCR sequencing and spatial transcriptomics to profile the transcriptomes of CD45+lymphocytes, with a particular focus on CD8+ T cells and their spatial interactions in the nasal mucosa of chronic rhinosinusitis with nasal polyps. Key findings ◾ An increased presence of granzyme K+(GZMK+) CD8+ T cells in nasal polyps ◾ GZMK+CD8+ T cells expressing CXCR4 and inducing the production of neutrophil chemoattractants in CXCL12-secreting fibroblasts ◾ GZMK+CD8+ T cells exhibiting a selective expansion of clones that recognize Epstein-Barr virus ◾ The GZMK+CD8+ T cell-fibroblast crosstalk observed in other inflammatory diseases. ➡️ More details: https://lnkd.in/eptwVGmw #HuazhongUniversityofScienceandTechnology #spatialomics #spatialbiology #singlecellanalysis #singlecell

📍 The single-cell transcriptome landscape in cardiovascular tissues and diseases Jiang et al. constructed an organized resource CardioAtlas, the first comprehensive and well-crafted reference atlas of cardiovascular systems and diseases. Key points ✅> 1,929,000 cells in 27 human data sets and > 1,088,000 cells in 39 mouse data sets ✅12 and 15 scRNA-seq atlases for human and mouse cardiovascular systems and diseases ✅Covering 43 and 39 cell types ✅Cell-type prediction, identification of marker genes, functional enrichment analysis, identification of cell-type-specific transcription regulons, and cell-cell communication analysis ✅Opportunity to upload scRNA-seq data for personalized analysis ✅Available at https://lnkd.in/eyxKaGAW ➡️ More details: https://lnkd.in/eEp-5y-H #HarbinMedicalUniversity #spatialomics #spatialbiology #singlecellanalysis #singlecell

📍 CZ CELLxGENE Discover: a single-cell data platform CZI Cell Science Program presented CZ CELLxGENE Discover, a data platform that provides curated and interoperable single-cell data.   Key points 📌 The largest collection of single-cell transcriptomic data (> 93 million unique cells) 📌 Curated, standardized and associated with consistent cell-level metadata 📌 Opportunity to explore individual datasets, perform cross-corpus analysis, and run meta-analyses 📌 Available at https://lnkd.in/e25PbWv9 ➡️ More details: https://lnkd.in/exsBhAyg #spatialomics #spatialbiology #singlecellanalysis #singlecell

📍 A panoramic view of cell population dynamics in mammalian aging Zhang et al. presented PanSci, a single-cell transcriptome atlas profiling over 20 million cells from 623 mouse tissues across different life stages, sexes, and genotypes.   Key points 🔹 More than 3,000 unique cellular states and over 200 aging-associated cell populations 🔹 Organ-, lineage-, and sex-specific shifts of cellular dynamics during lifespan progression 🔹 Systematic and organ-specific alterations in immune cell populations associated with aging 🔹 The regulatory roles of the immune system on aging 🔹 Specific age-related cell population expansions that are lymphocyte dependent. ➡️ More details: https://lnkd.in/ezNY98p2 #spatialomics #spatialbiology #singlecellanalysis #singlecell #TheRockefellerUniversity

💥 ASCA Moscow 2024 brought together young scientists from different countries Over 70 researchers and over 30 speakers joined the Workshop. The 5-day event’s program included lectures and hands-on bioinformatics, conference, partner’s exhibition and also parallel wet lab practices. 💬 Dr. Timur Fatkhudinov, Director of Research Institute of Molecular and Cellular Medicine, RUDN University: "Our colleagues from the Tomsk NRMC, who launched the first workshop on single-cell analysis 5 years ago, today, of course, become leaders in this field in our country, and I’m glad that RUDN University is involved in this activity." 💬 Dr. Evgeny Denisov, ASCA President: "In 5 years, we managed to gather a community of like-minded scientists in the field of single-cell analysis and bioinformatics, which has finally transformed into an international association that includes more than 270 people from 43 countries." ➡️ More details: https://lnkd.in/enFK-6aU #spatialomics #spatialbiology #singlecellanalysis #singlecell