Cytometry Part A

BackgroundImaging flow cytometry (IFC) provides a high quantity of single-cell morphological data, yet the field lacks open access tools for designing interpretable, bespoke parameters. In particular, rare and atypical cell populations where well annotated data is limited, are negatively affected. ResultsWe present Flow cytometry Feature Importance (FlowFI), an open-source graphical software for bespoke image parameter design and analysis. FlowFI provides a suite of image parameter options combining data across multiple channels and markers, tailored digital noise reduction (reducing noise resulting from common flow cytometry ultra-high image acquisition modalities), and a scalable, unsupervised feature selection pipeline that allows experimentalists to refine image-derived parameters iteratively, with a novel ensemble subsampling approach that provides robust feature importance scoring. We validated FlowFI using data from a rare and heterogenous bone marrow cell type, megakaryocytes, demonstrating that the tool can successfully identify novel, discriminatory morphological features to improve the purity of selected cell populations and gating strategy. ConclusionFlowFIs core functionalities are interacted with through an intuitive user interface for researchers with options to export data directly to common image and flow cytometry software formats. With this in mind, FlowFI offers a scalable way to both feature design, and feature refinement using a range of approaches to manifold learning, augmented by a data efficient bootstrap subsampling approach for unsupervised parameter recommendations in the big data regime. The software also introduces a new feature selection measures based on common manifold learning methods in the space inspired by the Uniform Manifold Approximation and Projection (UMAP) algorithm and finds performance comparable to existing methods. FlowFI provides a versatile testing ground for future developments in broad and dynamically developing areas of research including single cell analysis, label-free sorting and intra- and inter-cellular interaction analysis, while ensuring interoperability with current research workflows. Desktop installation options as well as detailed documentation can be found at https://github.com/EarlhamInst/FlowFI

Automated analysis of murine bronchoalveolar lavage fluid (BALF) cytology is important for preclinical respiratory research, yet progress has been limited by the lack of publicly available, well-annotated mouse BALF image datasets. We present MurineCyto-Det, a high-resolution murine BALF cytology dataset comprising 333 image tiles of size 1024x1024 pixels, annotated across five cytological categories with both pixel-level segmentation masks and one-to-one matched bounding boxes. The dataset contains 14,551 annotated cell instances and supports two complementary analysis tasks: morphology-oriented cell segmentation and object-level cell detection. To establish reproducible benchmark baselines, we evaluated representative segmentation and detection models. The results demonstrate the practical utility of MurineCyto-Det while highlighting realistic challenges arising from class imbalance, small object size, irregular cell morphology, and ambiguous debris-like structures. MurineCyto-Det provides a standardized resource for developing, evaluating, and comparing automated methods for murine BALF cytology analysis. The dataset is publicly available at https://doi.org/10.5281/zenodo.17608677.

Manufacturing red blood cells (RBCs) from human induced pluripotent stem cells (iPSCs) can improve our understanding of embryonic erythropoiesis, foster innovative treatments for RBC-related diseases, and ultimately address clinical blood supply shortages. However, existing systems face low efficiency, enucleation failure, and uncertainty about the develop-mental wave of cultured RBCs. We successfully used self-organized hemanoids to improve iPSC-derived RBC generation. Based on the hypothesis that cellular interactions and 3D organization promote hematopoietic cell fate, we aimed to thoroughly characterize hemanoids. We visualized the spatiotemporal emergence of hematopoiesis by generating a CD43-GFP reporter iPSC line. Imaging and spatial transcriptomics analysis provided de-tailed insight into the hemanoid architecture, identifying stromal cells and hepatoblasts as potential erythropoiesis-supportive elements. The developmental stage mirrors extraembryonic hematopoiesis. Given the difficulties of accessing these early stages in vivo, our system offers a platform not only for further clinical translation but also for exploring hu-man embryonic blood wave dynamics.

Current evaluation of male fertility is largely based on indirect sperm parameters such as viability, concentration, morphology, and motility; however, each of these parameters, alone or combined, has been shown to have limited predictive value for successful fertilization. To address this problem, we introduce hSPICER (human SPerm-Induced CEll-cell fusion Requiring JUNO), an assay that evaluates sperm function based on their ability to induce fusion of somatic cells expressing human JUNO (hJUNO), the egg-specific sperm receptor. Similarly to our previous discovery in mice, we found that human sperm can fuse with somatic cells expressing hJUNO on their surface (pseudo-eggs) and promote content mixing between cells in culture, as measured using a split GFP system. The assay is sensitive, specific, and species-dependent, requiring hJUNO for optimal signal. We generated a stable cell line expressing hJUNO, enhancing reproducibility and sensitivity. We also show that hSPICER is compatible with cryopreserved sperm and consistent over different days. Importantly, hSPICER values correlate with fertilization outcomes of patients during fertility treatments, indicating its potential as a functional diagnostic tool. Beyond diagnostic uses, hSPICER establishes a platform to explore sperm fusion mechanisms and to screen for therapeutic compounds and interventions to treat low fertility, enhance fertilization, and develop non-hormonal contraceptives for males and females, as well as quality assessment of semen samples in fertility clinics and sperm banks.

Metastasis remains the major cause of cancer-related mortality, and circulating tumor cells (CTCs) are both candidate liquid-biopsy biomarkers and plausible intermediates of metastatic dissemination. Because CTCs are extremely rare in peripheral blood, platform comparisons have often focused solely on recovery. That focus is insufficient for applications that depend on the quality of the recovered material, including single-cell profiling, short-term culture, and functional testing. Here, we compared four CTC isolation approaches: TellDx CTC System, Genesis System, RosetteSep, and flow cytometry, using spike-in experiments in human blood. Capture efficiency was evaluated across all four platforms; purity was assessed for TellDx, Genesis, and RosetteSep; and post-isolation GFP signal persistence in culture was assessed for TellDx and Genesis as an exploratory proxy for short-term post-isolation preservation. Under the conditions tested, TellDx showed the highest recovery (88.1% {+/-} 3.7%), followed by Genesis (40.6% {+/-} 12.1%), RosetteSep (36.5% {+/-} 9.0%), and flow cytometry (7.6% {+/-} 4.5%). TellDx also showed the highest purity score (3.76), whereas Genesis (2.25) and RosetteSep (2.09) did not differ substantially. In the short-term culture assay, TellDx-derived samples retained a higher normalized GFP signal than Genesis-derived samples at 48 h and 72 h. To synthesize these readouts, we propose the Recovery Performance Index (RPI), a composite score integrating recovery, purity, and post-isolation signal persistence. Within this experimental framework, TellDx achieved the highest RPI. These data support two conclusions. First, platform benchmarking for CTC workflows benefits from multidimensional evaluation rather than recovery alone. Second, under this spike-in model and within the specific workflows used here, TellDx performed best among the platforms tested. The principal contribution of this study is therefore the establishment of a practical benchmarking framework that can be expanded in future work using clinical samples, multiple CTC phenotypes, and orthogonal viability assays.

The binding of transmembrane (TM) ligands to their cognate TM receptors on neighboring cells governs intercellular adhesion and direct cell-cell communication. However, these interactions are difficult to study in vitro because they depend on membrane presentation, ligand orientation, receptor clustering, and avidity, features often not captured by soluble recombinant ligands or cell-free assays. Here, we describe a flow cytometry-based assay using fluorescent, lentiviral-derived virus-like particles (VLPs) displaying TM ligands to quantify binding to their receptors on target cells. Fluorescent VLPs are generated in-house by plasmid transfection in HEK293T cells and enable direct fluorescent detection without fluorochrome-conjugated secondary antibodies. The system is modular and readily accommodates engineered ligand constructs, including patient-derived variants. We applied this platform to generate ICAM-1-displaying fluorescent VLPs and to study human LFA-1 function in patient-derived leukocytes. This protocol provides a detailed workflow for VLP production and in vitro binding assays, offering a simple, quantitative, and cost-effective approach for studying TM ligand-receptor interactions in a membrane context. The system is well suited for mechanistic studies, functional assessment of patient-derived variants, and direct binding assays using patient-derived cells. Integrating the assay into multicolor flow cytometry panels enables simultaneous immunophenotyping and quantification of up to four ligand-receptor interactions at single-cell resolution. Key featuresO_LIQuantifies TM ligand-receptor binding in a membrane context using fluorescent VLPs and flow cytometry. C_LIO_LIFully in-house, modular system based on plasmid transfection in HEK293T cells, without reliance on recombinant ligands or fluorochrome-conjugated secondary antibodies. C_LIO_LISupports testing of engineered ligand variants, including patient-derived alleles, and direct functional studies on patient-derived cells. C_LIO_LICompatible with multicolor flow cytometry panels, enabling simultaneous immunophenotyping and quantification of up to four ligand-receptor interactions at single-cell resolution. C_LI Graphical overview O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/725198v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@a43069org.highwire.dtl.DTLVardef@166491borg.highwire.dtl.DTLVardef@49c7d4org.highwire.dtl.DTLVardef@1de36a0_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundSingle-cell multi-omics profiling of hematopoietic malignancies frequently involves pooling of patient samples before library preparation to reduce costs. Demultiplexing and quality control of the resulting sequencing data depend on experimental design, sequencing depth, and computational methods. Existing approaches benchmark individual tools, auto-select a single best method, or apply majority voting. However, none systematically exploit disagreement patterns among orthogonal strategies as a diagnostic signal for cell quality. ResultsWe introduce Split-flow, a modular Nextflow pipeline that runs hashing-based and SNP-based demultiplexing, and transcriptome-based doublet detection in parallel. It classifies cells into quality strata through a concordance-based decision framework. Validation on multiplexed CITE-seq data from 14 multiple myeloma patients across eight Chromium channels demonstrates high reproducibility and shows that discordant cells cluster within specific cell types and quality strata. TCR clonotype cross-referencing against VDJdb confirms that concordance-based classification enriches for biologically genuine immune receptor sequences, with a 5.3-fold enrichment of confirmed public TCR sequences in the high-confidence stratum. Downsampling analysis reveals that SNP-based methods are more depth-sensitive than hash-based approaches, supporting the recommendation to combine both strategies. The framework transfers to AML samples across three assay types (snMultiome-seq, scRNA-seq, scATAC-seq), where ATAC-based demultiplexing resolves donor assignment discordance under low hashing efficiency. ConclusionsSplit-flow demonstrates that combining of orthogonal preprocessing methods yields structured information about cell quality and offers a concordance-based framework that transforms this disagreement into a diagnostic signal. It introduces a preprocessing approach that can be exploited beyond hematopoietic malignancies in multiplexed single-cell applications. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/724135v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1f36dbcorg.highwire.dtl.DTLVardef@a9799forg.highwire.dtl.DTLVardef@6fca94org.highwire.dtl.DTLVardef@15cc1f3_HPS_FORMAT_FIGEXP M_FIG C_FIG Highlights and main findingsO_LIIntroduces Split-flow, a modular Nextflow DSL2 pipeline for preprocessing of multiplexed single-cell multi-omics sequencing data from hematopoietic malignancy samples via a post hoc concordance-based decision framework. C_LIO_LIProvides practical guidance for the experimental design of multiplexed single-cell multi-omics experiments, including the recommendation to combine antibody-based hashing with a SNP genotype reference for orthogonal demultiplexing. C_LIO_LIReveals that SNP-based demultiplexing is more sensitive to sequencing depth than hash-based approaches, and that the combined strategy mitigates depth-dependent biases in cell-type recovery. C_LIO_LIDemonstrates that disagreement between demultiplexing methods contains structured diagnostic information about cell quality, with concordance categories reflecting genuine quality gradients in multiple myeloma CITE-seq samples. C_LIO_LIValidates the concordance framework using T cell receptor sequences as an orthogonal biological readout, with a 5.3-fold enrichment of confirmed public TCR sequences in the high-confidence stratum. C_LIO_LIApplies the preprocessing framework to AML patient samples across three assay types (snMultiome-seq, scRNA-seq, and scATAC-seq) and demonstrates that ATAC-based demultiplexing can resolve donor-assignment discordance. C_LI

Single-cell RNA-sequencing-based characterization of cells that belong to the neoplastic clone is a major challenge in hematologic neoplasms, where malignant and normal cells coexist. Confident molecular profiling requires simultaneous analysis of gene expression and genetic mutations in individual cells, an ability that is not supported by the standard 10X Genomics workflow. Here, we developed a post-hoc targeted genotyping method for samples processed with the 10X Genomics 3 workflow. To establish the approach, we mixed two types of leukemic cells harboring distinct mutations and subjected them to single-cell RNA-sequencing. Repurposing an intermediate product of the experimental process allowed us to enrich for transcripts containing mutation sites. Long-read PacBio sequencing genotyped the transcripts and captured the associated cellular and molecular barcodes, allowing us to bioinformatically integrate the mutation and transcriptomic data at single-cell resolution. Our method demonstrates the detection of mast cell leukemia-associated point mutations in the KIT gene and chronic myeloid leukemia-associated BCR::ABL1 fusion transcripts. Single-cell analysis of primary leukocytes from chronic myeloid leukemia detected mutated cells at diagnosis, but not during imatinib treatment. Taken together, the method constitutes a broadly applicable framework for post-hoc genotyping of cells analyzed with single-cell RNA-sequencing.

High-dimensional single-cell technologies, such as flow cytometry and CITE-Seq, typically rely on established lineage markers to define cell identities. Additional markers are commonly analyzed within the context of these predefined cell types. Nonlinear projection methods such as t-SNE and UMAP provide a visual framework for this analysis by enabling the overlay of cell types and marker expression. However, these methods frequently produce projections where distinct cell types substantially overlap, hindering interpretation of marker expression patterns relative to known cell types. In this study, we investigate the underlying causes of this phenomenon and demonstrate that such overlaps often stem from the inherent high-dimensional structure of the data rather than limitations in the dimensionality reduction algorithms themselves. To address this, we introduce Cell Type Weighted Dimensionality Reduction (CWDR), a novel approach that incorporates lineage-based information through a supervised weighting mechanism. By integrating both cell identity and marker expression, CWDR preserves the visual separation between predefined cell types while maintaining the local variance necessary for downstream analysis. We validate our method across multiple high-dimensional flow cytometry and proteogenomic datasets. Our results show that CWDR significantly reduces inter-cluster overlap compared to traditional methods, providing a clearer framework for visualizing marker expression within the context of specific cell lineages.

In view of the outstanding progress of machine learning (ML) and growing cost of health systems, it is a current challenge to incorporate artificial intelligence tools into actual medical practice. Here we explored the feasibility and reliability of using machine learning to perform an important immunological investigation that currently requires experienced biologists : Anti-nuclear cytoplasmic antibodies (ANCAs) are important markers for vasculitis and they may be evidenced by microscopic examination of cells labeled with patients' sera. The use of a reliable ML classifier to discriminate between positive and negative samples would increase the rapidity and decrease the cost of immunofluorescence-based ANCA detection. Here, we tested seven well-documented ML algorithms, ranging from simple models such as k nearest neighbors to more complex convolutional neural networks involving millions of adjustable parameter. We studied the feasibility and reliability of classifying 1114 serum samples that had been collected for about 3 years and assayed with conventional procedure. We compared four strategies consisting of assaying either whole microscope fields or individual cell images, and natural images or histograms. The following conclusions were obtained : (i) Several different strategies allowed us to build models stable enough to discriminate between positive and negative samples collected during about 27 months, with a comparison to human classification yielding a kappa index of about 0.7, that may be considered as fairly good and intermediate between the performance of junior and senior biologists. (ii) Simpler ML models combined with theoretical thinking might provide the most rapid and efficient way of developing a reliable test within the framework of a single institution. (iii) In addition, the interpretability of the simplest model provided some theoretical insight into important classification parameters. (iv) An important point and caveat is that the multiplicity and versatility of currently available tools make it an essential requirement to test repeatedly a given model, that must be chosen as simple as possible, to achieve a reliability compatible with medical use. It is concluded that our study provides a strong incentive to incorporate ML tools in well defined medical tests, which might reduce the risk of human errors and pave the way to fully automatic procedures.

Understanding the behavior of - and {beta}-cells within intact human islets is essential for elucidating mechanisms of metabolic control in diabetes. Current cell-type identification strategies rely on destructive labeling or on advanced imaging modalities such as Fluorescence Lifetime Imaging Microscopy (FLIM), which provide rich metabolic information but require specialized instrumentation and acquisition protocols. Here we show that structured intracellular intensity patterns derived from endogenous autofluorescence are sufficient to discriminate and {beta} cells in living human islets. Using rotation-invariant Local Ternary Pattern (LTP) descriptors combined with morphological features, we achieve highly accurate classification (AUC = 0.92), improving upon previously reported benchmarks. The resulting framework is lightweight, interpretable, and compatible with standard imaging configurations, enabling accessible and scalable analysis of label-free microscopy data. Interpretability analyses demonstrate that discrimination is driven predominantly by fine-scale intracellular intensity organization rather than global morphology. In the spectral window employed, cytoplasmic autofluorescence is prominently shaped by lipofuscin-rich granules. Consistent with prior reports of higher lipofuscin accumulation in {beta}-cells, the dominant features identified here likely reflect differences in granule abundance and spatial organization between endocrine cell types. These findings indicate that endogenous intensity patterns encode sufficient structural information for reliable /{beta} discrimination, providing a biologically grounded and fully non-destructive framework for the identification of pancreatic islet cell types.

Background: Modern therapy for childhood and adolescent leukemia requires accurate risk classification of genomic subtype. Although short-read next-generation sequencing (NGS)- based approaches provide comprehensive clinical diagnostics in limited, highly resourced settings, they remain expensive, slow, and inaccessible to most children worldwide. Transformative approaches are needed to improve diagnostic classification for leukemia globally. Methods: We simultaneously continued to develop an analytical pipeline NASVar (Nanopore variant calling for adaptive sampling), and conducted a multicenter, type-two hybrid clinical validation study of an Oxford Nanopore Technologies (ONT) adaptive-sampling whole-genome sequencing (asWGS) assay across hospitals with varying diagnostic resources. In preparation for implementation, a global panel developed a leukemia-based standardized gene set and consensus laboratory-developed test (LDT) validation guidelines. Measures of assay effectiveness compared to both conventional and orthogonal NGS methods, where available, were simultaneously collected with data to measure the implementation outcomes of feasibility, fidelity, appropriateness, and cost. Results: All four centers successfully completed the LDT validation, with minimal adaptations required for regulatory compliance. A total of 457 specimens were sequenced (331 B-ALL, 83 AML, 43 T-ALL). For the 210 B-ALL cases with locally resolved genomic subtypes defined by DNA alterations, asWGS was 100% concordant (210/210). Cases locally defined as B-other were resolved via asWGS with disease-defining DNA alterations in 47% (49/105) of cases. An additional 41% (43/105) of locally defined B-other cases were classified by incorporation of DNA methylation, and all 16 B-ALL patient-derived xenograft controls were correct, for a total of 96% (318/331) of all B-ALL cases in the cohort resolved with single assay asWGS. For AML, 97% (56/58) of cases with locally resolved genomic subtypes were identified by automated asWGS analysis, while an additional two cases were identified after targeted manual review. At Indus Hospital in Pakistan, the B-ALL and AML diagnostic genomic subtype yield increased from 28% with local standard of care diagnostic testing, to 84% with asWGS. The cost of reagents and consumables in the United States, assuming pooled three-plexing, was $343/sample. Based on the combined hybrid validation results, all centers are independently preparing for clinical return of results. Conclusions: ONT asWGS was successfully validated as a clinical assay in four diverse hospital settings. As a single, multi-omic platform that delivers value across the continuum of high-resource to resource-limited contexts, the approach offers a disruptive solution to address the global equity gap in cancer diagnostics.

The ability to generate functional B cells from human pluripotent stem cells (hPSCs) would open new opportunities to develop novel B cell-based therapies to treat a range of human diseases and disorders. Towards this goal, we established a protocol that promotes the efficient development of B lineage cells from definitive hematopoietic progenitors generated from different hPSC lines. Flow cytometric and multi-omic scRNA-seq analyses revealed that B cell development from hPSCs transitions through the well-established pro-B, pre-B and naive B cell stages, accurately recapitulating B lymphopoiesis in the human adult bone marrow. Importantly, the naive B cells generated with this approach could be induced to mature into plasma cells that secrete antibodies and undergo class switching. Analyses of signaling pathways that regulate B lymphopoiesis in these cultures uncovered a potent inhibitory effect of IL-7 on functional IgH rearrangement, resulting in the development of abnormal cells that failed to undergo pre-B cell maturation. Finally, analysis of the different hPSC-derived hematopoietic programs revealed that both definitive and yolk sac progenitors display B cell potential, indicating that there are distinct developmental sources of human B lineage cells. Taken together, these findings demonstrate the efficient generation of B cells from hPSCs and, in doing so, provide a system for further investigating the earliest stages of human B lymphopoiesis and a source of appropriately staged plasma cells for future therapeutic applications.

How gene expression patterns change spatially as the embryo transitions from simple to complex structures remains a major developmental biology question. Recently developed imaging-based spatial transcriptomics (ST) enable mapping expression of multiple gene at a single-cell resolution. Although Xenopus is a key model in embryology there is no established ST pipeline, and commercially available techniques face many challenges (sample preparation, probe design, cell segmentation). Furthermore, the highly diverse cell shapes and sizes across developmental stages and between different tissues represent major hurdles to accurately defining cells. Here, we describe an optimized workflow for ST in blastula-to-tailbud-stage frog embryos using Merscope, commercial MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization) originally designed for standard mammalian tissues. With stringent quality control and tailored computational pipelines, we optimize this technology for robust, semi-quantitative profiling of spatial transcriptomic landscapes in non-mammalian embryos. Reliable tissue preservation and cell-segmentation enable high-resolution mapping of gene expression during the development of a complex multi-tissue organization. This versatile strategy applies broadly to various dynamic systems, from embryos of various model organisms to complex and heterogeneous organs in mammals. Summary statementThis Single-cell Spatial Transcriptomics pipeline and reference atlas in Xenopus - a model organism in embryology - overcome technical challenges and resolve dynamic changes in patterning during development.

To properly respond to their environment, cells adjust the activity of key regulatory proteins and rates of gene expression. Methods to detect and quantify these forms of regulatory dynamics in living cells are of central importance for understanding cellular signaling events in both physiological and pathological conditions. Current technologies in this field make use of fluorescent probes to track cell signaling dynamics. Although these technologies have been used for decades, challenges remain. In particular, the segmentation, tracking, and interpretation of single cell dynamic data are time-consuming, prone to subjective errors, and often lacking in standardization across experiments. Here, we present SPIFEE, a data pipeline that uses experiment-dependent parameters to smooth noise and quantify key features of fluorescence data from time-lapse imaging studies. Processing data in this manner enhances and accelerates quantification of live-cell gene and protein expression, simplifies data analysis, and facilitates hypothesis generation. Author SummaryCells adjust protein activity and gene expression levels over time to respond to changes in their environment, a process referred to as cell signaling dynamics. Quantifying cell signaling dynamics in living cells often uses fluorescent probes, such as green fluorescent protein (GFP) and its spectral variants, to track changes in gene expression or protein activity over time. Challenges inherent in analyzing fluorescence data from single cells stem from biological and experimental noise, time-consuming quantification, and subjective errors. To address these challenges, we developed a computational tool called Signal Processing and Integrated Feature Extraction (SPIFEE). The pipeline improves the quality of fluorescence data analysis by reducing noise and extracting signal features in a way that is both intuitive and objective. The pipeline provides more accurate, rapid, and unbiased quantification of time-lapse microscopy data.

Abstract Background: Mean platelet volume (MPV) is a simple, low-cost biomarker that reflects platelet activation. Its prognostic value in septic shock remains controversial. We aimed to determine whether MPV at intensive care unit (ICU) admission is associated with hospital mortality in patients with septic shock. Methods: Retrospective cohort study of consecutive adults with septic shock (Sepsis-3 criteria) admitted to a single ICU. MPV, severity scores (SOFA, APACHE II, SAPS II), procalcitonin, and clinical data were collected. The primary outcome was in-hospital mortality. Spearman correlation, univariate and multivariate logistic regression (with Firth's correction), ROC curves, and subgroup analyses were performed. Results: Fifty-eight patients were included; mortality was 58.6%. MPV did not differ between non-survivors and survivors (13.09 {+/-} 1.37 vs. 12.66 {+/-} 1.45 fL, p = 0.259). MPV showed a weak correlation with procalcitonin ({rho} = 0.394, p = 0.002) but not with severity scores. In multivariate analysis adjusting for age, sex, SOFA and comorbidity count, MPV was not an independent predictor of mortality (OR 1.075, 95% CI 0.682-1.755, p = 0.749). The area under the ROC curve for MPV was 0.598 (95% CI 0.444-0.752), significantly lower than that of SOFA (0.837) and procalcitonin (0.836). Subgroup analyses showed no significant association between MPV and mortality in any stratum. Conclusions: In this cohort of septic shock patients, MPV at ICU admission was not associated with hospital mortality and had poor discriminative ability. Widely used severity scores and procalcitonin remain superior prognostic markers. MPV should not be used as a prognostic tool in septic shock. Keywords: Septic shock, Mean platelet volume, Mortality, SOFA, Procalcitonin, Biomarker

Different omics types such as genomics and proteomics all contribute to deciphering biology. Applying these omics approaches in a spatial context helps reveal biology in situ at a single cell level. Here we present a protocol for the combined multiplexed detection of targeted genes using DNA FISH, and proteins using multiplexed immunofluorescence. The protocol is integrated on the commercial PhenoCycler platform and generates one single dataset with gene and protein readout at a single cell level in large tissue sections, allowing for a throughput of thousands to millions of cells. The workflow can be used for characterising malignant cells in large tumor areas based on genetic aberrations, while deciphering the cellular landscape and microenvironment from multiplexed protein detection using immunofluorescence.

BackgroundCytokines are immunomodulatory proteins that play central roles in regulating immune responses and represent attractive targets for cancer therapy. However, as single agents, cytokines have shown limited clinical benefit due to systemic toxicities and a short in vivo half-life. Our group has focused on engineering fusion cytokines (fusokines) that couple two cytokines into a single biologic to reprogram immune cell responses by enforcing non-canonical receptor engagement and signaling. A chimeric IL-6/IL-1{beta} fusokine was engineered to test the hypothesis that enforced co-engagement of IL-6 and IL-1{beta} signaling pathways would confer a gain-of-function phenotype in T cells and promote robust anti-tumor immunity. Here, we describe the immunomodulatory properties of IL6/1 fusokine and a method to deliver this fusokine to produce inhibition of ovarian tumor growth in a pre-clinical mouse model. MethodsLentiviral vectors encoding murine or human IL6/1 were designed using Vector Builder and expressed in either HEK293, CHO or ID8-F3 (p53-/-) cells depending on the downstream experiment to be conducted. IL6/1 expression was validated by ELISA and flow cytometry. Effects of human IL6/1 (hIL6/1) on T cell function (proliferation, memory phenotype, activation induced apoptosis) were monitored by flow cytometry. For in vivo studies, ID8-F3 murine ovarian cancer cells expressing mouse IL6/1 (mIL6/1) were administered intraperitoneally (I.P.) as a cell-based therapy to C57BL/6 female mice bearing established ID8-F3 luciferase tumors. Tumor progression was monitored by bioluminescence (BLI) imaging, and overall survival was evaluated. ResultshIL6/1 significantly enhanced T cell survival and selectively promoted activation and expansion of CD45RO memory T cells. mIL6/1 expressing ID8-F3 cells (ID8IL6/1) demonstrated stable transduction and sustained cytokine secretion. In vivo, ID8IL6/1 cell therapy significantly reduced tumor growth and improved overall survival compared to control groups, with 2 of 8 mice achieving complete tumor clearance. ConclusionThese findings indicate that IL6/1 fusokine enhances T cell survival and proliferation while promoting memory responses. Engineered cancer cells (ID8-F3) expressing mIL6/1 fusokine induced a strong anti-tumor response when delivered as a therapeutic vaccine in ovarian cancer mouse model. What is already known on this topicO_LIFusokines are a class of bifunctional proteins designed to achieve synergistic immune modulation. Previous studies in our lab have shown fusokine exhibit gain-of-function immunomodulating activity. Individually, IL-6 and IL-1{beta} are recognized for their roles in promoting T-cell proliferation and effector function. However, the potential for a fused IL-6/1 fusokine to reprogram the immune system and elicit a superior anti-tumor response in vivo in ovarian cancer model is not yet studied. C_LI What this study addsO_LIThis study develops a novel fusion cytokine (fusokine), combining IL-6 and IL-1{beta}, and demonstrate robust activation of T cells. In a preclinical ovarian cancer model, engineered cancer cells expressing IL6/1 used as a therapeutic vaccine showed significant tumor reduction and improved overall survival. C_LI How this study might affect research, practice or policyO_LIThis study demonstrates that in comparison to individual cytokines, fusokines have greater potential to activate T cell function and when delivered as a cell therapy, achieve clear therapeutic efficacy in an ovarian cancer model. Further translational and clinical studies may enable the development of novel and more effective fusokine cell therapy approaches for patients with ovarian cancer. C_LI

Celiac disease (CD) is strongly associated with specific HLA DQ heterodimers, formed by HLA DQA1 and HLA DQB1 proteins. In particular DQ2.5 (DQB1*02 associated to DQA1*05) and DQ8 (DQB1*03:02 with DQA1*03) are present in virtually all celiac patients. HLA DQB1*02 is considered the main single genetic susceptibility marker and has been reported in 90 to 95% of CD patients. However, the distribution of these alleles may vary across populations, potentially impacting the performance of genetic screening strategies. In this study, we evaluated the prevalence of HLA DQ2.5 and DQ8 genotypes in celiac patients (n = 41) and an unbiased general population cohort (n = 60) from San Luis, Argentina, using a PCR-based genotyping approach. In addition, we assessed the feasibility of a simplified saliva direct PCR protocol for large scale testing. Overall, 95.1% of CD patients carried DQ2.5 and/or DQ8. Notably, 41.5% of patients were DQ8(+)/DQ2.5(-), and 36.6% lacked the DQB1*02 allele, indicating that DQB1*02 based screening alone would have reduced sensitivity in this population. In the general population, 53.3% of individuals carried CD associated genotypes, with a markedly higher prevalence of DQ8 compared to European cohorts. Genotype distributions deviated from Hardy Weinberg equilibrium in CD patients but not in the general population. We show that DQB1*03:02 is a reliable proxy for DQ8, allowing simplification of genotyping strategies, whereas DQA1*05 typing remains essential to discriminate DQ2.5 from other lower risk DQB1*02 carrying heterodimers. We also describe a saliva direct PCR approach showing a performance comparable to purified DNA based assays. These findings highlight the importance of population specific genetic data for optimizing CD screening strategies and foster the development of simplified, cost effective genotyping approaches for large scale applications.

During early development of the placenta, a subset of murine trophectoderm stem cells (TSCs) undergo endoreplication, an unusual form of cell division cycle that decouples DNA synthesis from cytokinesis, resulting in physiological polyploidy. Oscillations in CDK2 activity are essential for the orderly progression of the cell cycle to ensure replicated DNA is accurately partitioned into two daughter cells. However, it remains underexplored how the dynamics of CDK2 activity regulate endoreplication in the context of TSCs differentiation. To address this question, we leveraged the variability in cell fate decisions in an established in vitro system of TSCs differentiation that relies on removal of a growth factor, FGF4, to induce endoreplication. Using quantitative single-cell live confocal microscopy of a precise CDK2 biosensor, DHB-Venus, we identified at least three different outcomes upon FG4 removal: self-renewal, endoreplication, and migration. Our quantitative analyses showed high levels of Cdk2 activity in self-renewing cells whereas intermediate DHB-Venus turnover is linked to increased nuclear and cell size, indicating a shift to endoreplication. Importantly, we also characterize a third class of differentiating TSCs with migratory characteristics that correlate with low levels Cdk2 activity without a change in nuclear size. In sum, our results demonstrated a correlation between different fate outcomes and specific thresholds of CDK2 activity. Our findings show that TSCs can distinguish between different outcomes through modulating the central kinase of the cell cycle, CDK2, positioning it as a key regulator of early trophoblast differentiation. Summary StatementThis study investigates the oscillatory behavior of CDK2 activity during murine trophectoderm differentiation and its potential role in guiding cell fate decisions.