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Vakkilainen, S.

Publications and source records attributed to Vakkilainen, S..

2 recordsLinked to original sources

Cumulative microscopy reveals cellular states in fibroblasts from patients with genetic disorders

Analysis of cellular states and signaling trajectories can provide insights into causes of disease. We developed cumulative microscopy, a method to perform cyclical imaging without elution or quenching steps. Cumulative microscopy computationally extracts individual signals from accumulating fluorescence during sequential imaging. We used cumulative microscopy to quantitatively assess cell cycle and stress markers in individual primary fibroblasts from patients with rare genetic proliferative disorders with increased cancer risk. Neural network-based analysis of cumulative microscopy data suggested that cells from patients with Cartilage-hair hypoplasia (CHH), but not Mulibrey Nanism (MUL), showed replication stress. We analyzed cell states and cell trajectories and found that a subset of cells from patients with CHH showed spontaneous replication stress, followed by cell cycle exit in both G1 and G2 phase. We note that replication stress potentially could underlie both proliferative defects and increased cancer risk in CHH patients and conclude that cumulative microscopy is an efficient, quantitative, and generalizable approach to multiplex microscopy. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/668398v1_ufig1.gif" ALT="Figure 1"> View larger version (80K): org.highwire.dtl.DTLVardef@4bcd0dorg.highwire.dtl.DTLVardef@83262corg.highwire.dtl.DTLVardef@3f3f13org.highwire.dtl.DTLVardef@46174b_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

Completeness estimation of large-scale single-cell sequencing projects

During embryonic development, cells undergo differentiation into highly specialized cell types. Capitalizing on single-cell RNA sequencing, many initiatives and substantial resources have been established for cataloguing these differentiated cell types by their transcriptomic profiles. Despite the extensive efforts to profile various organs and their cellular compositions, we lack metrics to assess the completeness of the sequencing projects. In this cellular biodiversity analysis, we leveraged the increasingly available single-cell data together with statistical methods, originally developed for assessing the species richness of ecological communities, to estimate the cellular diversity of any organ based on current data from single-cell profiling technologies. Deriving from such cellular richness estimates, we established a practical statistical framework that enables reliable assessment of the completeness of any large-scale single-cell profiling projects, after which additional sequencing efforts do not anymore reveal new insights into an organs cellular composition. Such estimates can serve as stoppage-points for the ongoing sequencing projects, hence guiding a more cost-efficient completion of the profiling of various human tissues.

cell biology↗