Biology subjects

Graf, W.

Publications and source records attributed to Graf, W..

10 recordsLinked to original sources

bioRxiv2024

Cellular behavior analysis from live-cell imaging of TCR T cell-cancer cell interactions

T cell therapies, such as chimeric antigen receptor (CAR) T cells and T cell receptor (TCR) T cells, are a growing class of anti-cancer treatments. However, expansion to novel indications and beyond last-line treatment requires engineering cells dynamic population behaviors. Here we develop the tools for cellular behavior analysis of T cells from live-cell imaging, a common and inexpensive experimental setup used to evaluate engineered T cells. We first develop a state-of-the-art segmentation and tracking pipeline, Caliban, based on human-in-the-loop deep learning. We then build the Occident pipeline to collect a catalog of phenotypes that characterize cell populations, morphology, movement, and interactions in co-cultures of modified T cells and antigen-presenting tumor cells. We use Caliban and Occident to interrogate how interactions between T cells and cancer cells differ when beneficial knock-outs of RASA2 and CUL5 are introduced into TCR T cells. We apply spatiotemporal models to quantify T cell recruitment and proliferation after interactions with cancer cells. We discover that, compared to a safe harbor knockout control, RASA2 knockout T cells have longer interaction times with cancer cells leading to greater T cell activation and killing efficacy, while CUL5 knockout T cells have increased proliferation rates leading to greater numbers of T cells for hunting. Together, segmentation and tracking from Caliban and phenotype quantification from Occident enable cellular behavior analysis to better engineer T cell therapies for improved cancer treatment.

cell biology↗

bioRxiv2024

Maturation of Human Intestinal Epithelial Cell Layers Fortifies the Apical Surface against Salmonella Attack

The active invasion of intestinal epithelial cells (IECs) represents a key event in the infection cycle of many gut pathogens. Studies of how Salmonella enterica Typhimurium (S.Tm) bacteria enter transformed cell lines have shaped the paradigm for swift type-three-secretion-system-1 (TTSS-1)-driven IEC invasion, fueled by expansive membrane ruffles. However, comparative studies suggest that non-transformed IECs in the intact gut context comprise a much more challenging target for the attack. The molecular and cellular features that explain these discrepancies remain undefined. By live-cell imaging in human enteroid-and colonoid-derived IEC layers, we demonstrate that the maturation state of gut epithelia dramatically impacts permissiveness to S.Tm invasion. IEC layers kept under immature progenitor-cell-promoting conditions are permissive to the bacterial invasion, whereas maturation towards an enterocyte/colonocyte fate reduces the frequency of S.Tm-induced epithelial entry structures, and lowers the invasion efficiency by up to tenfold. This phenotypic shift during IEC maturation couples to an altered expression of actin regulatory proteins implicated in the invasion process, and an increased dependence on the S.Tm TTSS-1 effector SipA for successful entry. In addition, IEC maturation involves upregulation of cell surface mucins, e.g. MUC13, and shifts in glycocalyx composition, as revealed by multiple lectin stainings. Enzymatic treatment of the apical surface with the StcE mucinase converts maturing IEC layers back to the S.Tm-invasion-permissive state of their immature counterparts. Taken together, these results showcase how the maturation state of human IECs dictates the susceptibility to invasion by a prototype enterobacterium.

cell biology↗

bioRxiv2024

Determinants of the Divergent Salmonella and Shigella Epithelial Colonization Strategies Resolved in Human Enteroids and Colonoids

Despite close relatedness, the major enteropathogens Salmonella and Shigella differ in infectious dose, pathogenesis, and disease kinetics. The prototype strains Salmonella enterica serovar Typhimurium (Salmonella) and Shigella flexneri (Shigella) use Type-3-secretion-systems (T3SSs) to colonize intestinal epithelial cells (IECs), but have evolved partially unique sets of T3SS effectors and accessory virulence factors. A synthesis of how these differences impact the temporal progression of infection in non-transformed human epithelia is missing. Here, we followed Salmonella and Shigella infections of human enteroids and colonoids by time-lapse imaging to pinpoint virulence factor modules that shape the divergent epithelial colonization strategies. By an apical targeting module that integrates flagella and the SPI-4-encoded adhesin system with T3SS, Salmonella accomplishes appreciable numbers of apical invasion events, promptly terminated by IEC death, and thus fostering a polyclonal iterative epithelial colonization strategy. The lack of a corresponding module in Shigella makes this pathogen reliant on external factors such as preexisting damage for rare apical access to the intraepithelial environment. However, Shigella compensates for this ineptness by an intraepithelial expansion module, where tight coupling of OspC3-dependent temporal delay of cell death and IcsA-mediated lateral spread enables intraepithelial Shigella to outrun the IEC death response, fostering an essentially monoclonal colonization strategy.

microbiology↗

bioRxiv2021

Spatio-temporal coordination at the maternal-fetal interface promotes trophoblast invasion and vascular remodeling in the first half of human pregnancy

Beginning in the first trimester, fetally derived extravillous trophoblasts (EVTs) invade the uterus and remodel its spiral arteries, transforming them into large, dilated blood vessels left with a thin, discontinuous smooth muscle layer and partially lined with EVTs. Several mechanisms have been proposed to explain how EVTs coordinate with the maternal decidua to promote a tissue microenvironment conducive to spiral artery remodeling (SAR). However, it remains a matter of debate which immune and stromal cell types participate in these interactions and how this process evolves with respect to gestational age. Here, we used a multiomic approach that combined the strengths of spatial proteomics and transcriptomics to construct the first spatiotemporal atlas of the human maternal-fetal interface in the first half of pregnancy. We used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody panel to analyze [~]500,000 cells and 588 spiral arteries within intact decidua from 66 patients between 6-20 weeks of gestation, integrating this with coregistered transcriptomic profiles. Gestational age substantially influenced the frequency of many maternal immune and stromal cells, with tolerogenic subsets expressing CD206, CD163, TIM-3, Galectin-9, and IDO-1 increasingly enriched and colocalized at later time points. In contrast, SAR progression preferentially correlated with EVT invasion and was transcriptionally defined by 78 gene ontology pathways exhibiting unique monotonic and biphasic trends. Lastly, we developed an integrated model of SAR supporting an intravasation mechanism where invasion is accompanied by upregulation of pro-angiogenic, immunoregulatory EVT programs that promote interactions with vascular endothelium while avoiding activation of immune cells in circulating maternal blood. Taken together, these results support a coordinated model of decidualization in which increasing gestational age drives a transition in maternal decidua towards a tolerogenic niche conducive to locally regulated, EVT-dependent SAR.

systems biology↗

bioRxiv2021

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource.

bioinformatics↗

bioRxiv2020

Multiplexed imaging of human tuberculosis granulomas uncovers immunoregulatory features conserved across tissue and blood

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that is distinctly characterized by granuloma formation within infected tissues. Granulomas are dynamic and organized immune cell aggregates that limit dissemination, but can also hinder bacterial clearance. Consequently, outcome in TB is influenced by how granuloma structure and composition shift the balance between these two functions. To date, our understanding of what factors drive granuloma function in humans is limited. With this in mind, we used Multiplexed Ion Beam Imaging by Time-of-Flight (MIBI-TOF) to profile 37 proteins in tissues from thirteen patients with active TB disease from the U.S. and South Africa. With this dataset, we constructed a comprehensive tissue atlas where the lineage, functional state, and spatial distribution of 19 unique cell subsets were mapped onto eight phenotypically-distinct granuloma microenvironments. This work revealed an immunosuppressed microenvironment specific to TB granulomas with spatially coordinated co-expression of IDO1 and PD-L1 by myeloid cells and proliferating regulatory T cells. Interestingly, this microenvironment lacked markers consistent with T-cell activation, supporting a myeloid-mediated mechanism of immune suppression. We observed similar trends in gene expression of immunoregulatory proteins in a confirmatory transcriptomic analysis of peripheral blood collected from over 1500 individuals with latent or active TB infection and healthy controls across 29 cohorts spanning 14 countries. Notably, PD-L1 gene expression was found to correlate with TB progression and treatment response, supporting its potential use as a blood-based biomarker. Taken together, this study serves as a framework for leveraging independent cohorts and complementary methodologies to understand how local and systemic immune responses are linked in human health and disease.

immunology↗

bioRxiv2019

Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

While live-cell imaging is a powerful approach to studying the dynamics of cellular systems, converting these imaging data into quantitative, single-cell records of cellular behavior has been a longstanding challenge. Deep learning methods have proven capable of performing cell segmentation--a critical task for analyzing live-cell imaging data--but their performance in cell tracking has been limited by a lack of dynamic datasets with temporally consistent single-cell labels. We bridge this gap through the integrated development of labeling and deep learning methodology. We present a new framework for scalable, human-in-the-loop labeling of live-cell imaging movies, which we use to label a large collection of movies of fluorescently labeled cell nuclei. We use these data to create a new deep-learning-based cell-tracking method that achieves state-of-the-art performance in cell tracking. We have made all of the data, code, and software publicly available with permissive open-source licensing through the DeepCell projects web portal https://deepcell.org.

systems biology↗

bioRxiv2019

DNA barcode reference libraries for the monitoring of aquatic biota in Europe: Gap-analysis and recommendations for future work

Effective identification of species using short DNA fragments (DNA barcoding and DNA metabarcoding) requires reliable sequence reference libraries of known taxa. Both taxonomically comprehensive coverage and content quality are important for sufficient accuracy. For aquatic ecosystems in Europe, reliable barcode reference libraries are particularly important if molecular identification tools are to be implemented in biomonitoring and reports in the context of the EU Water Framework Directive (WFD) and the Marine Strategy Framework Directive (MSFD). We analysed gaps in the two most important reference databases, Barcode of Life Data Systems (BOLD) and NCBI GenBank, with a focus on the taxa most frequently used in WFD and MSFD. Our analyses show that coverage varies strongly among taxonomic groups, and among geographic regions. In general, groups that were actively targeted in barcode projects (e.g. fish, true bugs, caddisflies and vascular plants) are well represented in the barcode libraries, while others have fewer records (e.g. marine molluscs, ascidians, and freshwater diatoms). We also found that species monitored in several countries often are represented by barcodes in reference libraries, while species monitored in a single country frequently lack sequence records. A large proportion of species (up to 50%) in several taxonomic groups are only represented by private data in BOLD. Our results have implications for the future strategy to fill existing gaps in barcode libraries, especially if DNA metabarcoding is to be used in the monitoring of European aquatic biota under the WFD and MSFD. For example, missing species relevant to monitoring in multiple countries should be prioritized. We also discuss why a strategy for quality control and quality assurance of barcode reference libraries is needed and recommend future steps to ensure full utilization of metabarcoding in aquatic biomonitoring.

ecology↗

bioRxiv2018

DeepCell 2.0: Automated cloud deployment of deep learning models for large-scale cellular image analysis

Deep learning is transforming the analysis of biological images but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 106 1-megapixel images in ~5.5 h for ~$250, with a sub-$100 cost achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console; a persistent deployment is available at https://deepcell.org.

systems biology↗

bioRxiv2018

Late greenhouse gas mitigation has heterogeneous effects on European caddisfly diversity patterns

Little remains known about how the timing of mitigation of current greenhouse gas emissions will influence freshwater biodiversity patterns. Using three general circulation models, we evaluate the response of 260 broad-ranging European caddisfly species to climate conditions in 2080 under two scenarios: business as usual (A2A) and mitigation (A1B). If implemented effectively, recent government commitments established under COP21, to mitigate current greenhouse gas emissions, would result in future climatic conditions similar to the mitigation scenario we explored. Under the Cgcm circulation model, which we found to be the most conservative model, suitable environmental conditions were predicted to shift 3{degrees} more to the east under the mitigation scenario compared to business as usual. The majority of broad-ranging European caddisfly species will benefit from mitigation, but 5 to 15% of species that we evaluated will be bigger losers under the mitigation scenario compared to business as usual. Under the mitigation scenario, caddisfly species that will retain less of their current range and experience lower predicted range expansion are those that currently have relatively limited distributions. Continental-scale assessments such as the ones that we present are needed to identify species at greatest risk of range loss under changing climatic conditions.

ecology↗