Biology subjects

Irish, J. M.

Publications and source records attributed to Irish, J. M..

15 recordsLinked to original sources

bioRxiv2026

Single Cell Cytometry of Mouse Brain Tissue

Suspension mass cytometry can measure 50 or more markers per cell and is not impacted by cellular autofluorescence, making it an appealing platform for comprehensive phenotyping of single cells in tissue with high innate fluorescence, such as brain. However, this organ is challenging to reduce to a single cell suspension: it is a deeply interconnected tissue rich in extracellular material and contains a wide variety of cell types including neurons, their supporting glia, and hematopoietic cells from peripheral circulation, any or all of which may have competing requirements for survival through the dissociation process. This study compares approaches for producing single-cell preparations from white and gray matter enriched regions of adult mouse brain, testing simple mechanical dissociation, Accutase, and three gentle enzymes plus DNase: papain, collagenase II, and collagenase plus dispase, all with or without Percoll gradient centrifugation to separate intact cells from debris. Live cells were counted by hemocytometer and Trypan blue exclusion to assess yield and compare across enzyme conditions. Mass cytometry was used to assess cellular composition after representative dissociation, debris removal, and viable cryopreservation. Key proteins measured included beta III-tubulin for neurons, glial fibrillary acidic protein (GFAP) for astrocytes, oligodendrocyte transcription factor (OLIG2) for oligodendrocytes, CD31 for endothelial cells, CD24 for ependymal cells, purinergic receptor P2Y12 (P2RY12) for microglia, and CD45 for leukocytes. Mechanical disaggregation paired with enzymatic dissociation by papain, Collagenase/Disapse, or Collagenase II plus DNase produced approximately 400,000 live cells per 100 grams of tissue. Cell yield was not significantly different between conditions and yields were similar between white and grey matter. Addition of Percoll gradient centrifugation substantially reduced the number of viable cells recovered (average decrease of 88%) and resulted in viable cell numbers that were insufficient for robust analysis of less common cell subsets. In addition to neurons, mass cytometry identified six major populations of non-neuronal cells: oligodendrocytes, astrocytes, endothelial cells, ependymal cells, microglia, and leukocytes. The relative abundance of these six populations was generally unchanged by cryopreservation in both grey matter (r = 0.95), and white matter (r = 0.90), though loss of oligodendrocytes did occur in both tissues. Proliferation, as measured by Ki67 positivity, was non-existent; consistent with this, levels of phosphorylated STAT1 (p-STAT1) and p-STAT3 proteins were low in all cell populations and unchanged by cryopreservation. A modest increase in p-S6(S235/236) after cryopreservation was consistently observed only in microglia. Together, these results establish standardized procedures for generating viable single cell suspensions for cytometry that preserve the cellular diversity of the adult murine brain with minimal perturbation of intracellular signaling cascades.

neuroscience↗

bioRxiv2026

Critical illness expands a transcriptionally distinct hypometabolic CD8+ T effector program associated with respiratory failure and mortality

Immune dysfunction is a major driver of morbidity and mortality in critical illness syndromes including sepsis. Specifically, CD8+ T cell dysfunction has been linked to organ failure and death. To characterize the immune substructure of circulating CD8+ T cells in critical illness at high dimension, we used single-cell RNA sequencing of peripheral blood CD8+ T cells from 38 critically ill patients and 9 healthy controls. We annotated seven CD8+ T cell clusters, which included a CD8+ effector subset, termed T effector state 2 (TEff-2), that was only present in critically ill patients and associated with more severe respiratory failure and higher mortality. TEff-2 showed effector activation and inflammatory stress conditioning yet had markedly reduced metabolic transcripts without canonical features of exhaustion. Trajectory analyses positioned TEff-2 as a terminal CD8+ T effector cell fate driven in part by DDIT4 and DUSP1, which negatively regulate mTOR and MAPK signaling, respectively. Interestingly, this transcriptional program was indistinguishable by classical protein cytometry methods. These results, including the mortality association, were validated in a larger (n=91) independent external cohort of critically ill patients with sepsis. In summary, TEff-2 represents a latent transcriptional program that delineates a clinically high-risk CD8+ T cell state in critical illness.

immunology↗

bioRxiv2024

Velociraptor: Cross-Platform Quantitative Search Using Hallmark Cell Features

A key challenge for single cell discovery analysis is to identify new cell types, describe them quantitatively, and seek these novel cells in new studies often using a different platform. Over the last decade, tools were developed to address identification and quantitative description of cells in human tissues and tumors. However, automated validation of populations at the single cell level has struggled due to the cytometry fields reliance on hierarchical, ordered use of features and on platform-specific rules for data processing and analysis. Here we present Velociraptor, a workflow that implements Marker Enrichment Modeling in three cross-platform modules: 1) identification of cells specific to disease states, 2) description of hallmark features for each cell and population, and 3) searching for cells matching one or more hallmark feature sets in a new dataset. A key advance is that Velociraptor registers cells between datasets, including between flow cytometry and quantitative imaging using different, overlapping feature sets. Four datasets were used to challenge Velociraptor and reveal new biological insights. Working at the individual sample level, Velociraptor tracked the abundance of clinically significant glioblastoma brain tumor cell subsets and characterized the cells that predominate in recurrent tumors as a close match for rare, negative prognostic cells originally observed in matched pre-treatment tumors. In patients with inborn errors of immunity, Velociraptor identified genotype-specific cells associated with GATA2 haploinsufficiency. Finally, in cross-platform analysis of immune cells in multiplex imaging of breast cancer, Velociraptor sought and correctly identified memory T cell subsets in tumors. Different phenotypic descriptions generated by algorithms or humans were shown to be effective as search inputs, indicating that cell identity need not be described in terms of per-feature cutoffs or strict hierarchical analyses. Velociraptor thus identifies cells based on hallmark feature sets, such as protein expression signatures, and works effectively with data from multiple sources, including suspension flow cytometry, imaging, and search text based on known or theoretical cell features.

systems biology↗

bioRxiv2024

Single Cell Profiling Distinguishes Leukemia-Selective Chemotypes

A central challenge in chemical biology is to distinguish molecular families in which small structural changes trigger large changes in cell biology. Such families might be ideal scaffolds for developing cell-selective chemical effectors - for example, molecules that activate DNA damage responses in malignant cells while sparing healthy cells. Across closely related structural variants, subtle structural changes have the potential to result in contrasting bioactivity patterns across different cell types. Here, we tested a 600-compound Diversity Set of screening molecules from the Boston University Center for Molecular Discovery (BU-CMD) in a novel phospho-flow assay that tracked fundamental cell biological processes, including DNA damage response, apoptosis, M-phase cell cycle, and protein synthesis in MV411 leukemia cells. Among the chemotypes screened, synthetic congeners of the rocaglate family were especially bioactive. In follow-up studies, 37 rocaglates were selected and deeply characterized using 12 million additional cellular measurements across MV411 leukemia cells and healthy peripheral blood mononuclear cells. Of the selected rocaglates, 92% displayed significant bioactivity in human cells, and 65% selectively induced DNA damage responses in leukemia and not healthy human blood cells. Furthermore, the signaling and cell-type selectivity were connected to structural features of rocaglate subfamilies. In particular, three rocaglates from the rocaglate pyrimidinone (RP) structural subclass were the only molecules that activated exceptional DNA damage responses in leukemia cells without activating a detectable DNA damage response in healthy cells. These results indicate that the RP subset should be extensively characterized for anticancer therapeutic potential as it relates to the DNA damage response. This single cell profiling approach advances a chemical biology platform to dissect how systematic variations in chemical structure can profoundly and differentially impact basic functions of healthy and diseased cells.

systems biology↗

bioRxiv2024

IL-8 Instructs Macrophage Identity in Lateral Ventricle Contacting Glioblastoma

Adult IDH-wildtype glioblastoma (GBM) is a highly aggressive brain tumor with no established immunotherapy or targeted therapy. Recently, CD32+ HLA-DRhi macrophages were shown to have displaced resident microglia in GBM tumors that contact the lateral ventricle stem cell niche. Since these lateral ventricle contacting GBM tumors have especially poor outcomes, identifying the origin and role of these CD32+ macrophages is likely critical to developing successful GBM immunotherapies. Here, we identify these CD32+ cells as M_IL-8 macrophages and establish that IL-8 is sufficient and necessary for tumor cells to instruct healthy macrophages into CD32+ M_IL-8 M2 macrophages. In ex vivo experiments with conditioned medium from primary human tumor cells, inhibitory antibodies to IL-8 blocked the generation of CD32+ M_IL-8 cells. Finally, using a set of 73 GBM tumors, IL-8 protein is shown to be present in GBM tumor cells in vivo and especially common in tumors contacting the lateral ventricle. These results provide a mechanistic origin for CD32+ macrophages that predominate in the microenvironment of the most aggressive GBM tumors. IL-8 and CD32+ macrophages should now be explored as targets in combination with GBM immunotherapies, especially for patients whose tumors present with radiographic contact with the ventricular-subventricular zone stem cell niche. - IL-8 is expressed by GBM cells and enriched in lateral ventricle-contacting tumors - M_IL-8 macrophages are CD32+ HLA-DR++ CD163+ CD206+ CD86- PD-L1- - M_IL-8 macrophages instructed with IL-8 or GBM conditioned medium match human glioblastoma associated macrophages - IL-8 is necessary for GBM tumor cells to generate M_IL-8 macrophages

cancer biology↗

bioRxiv2023

Dephosphorylation of 4EBP1/2 Induces Prenatal Neural Stem Cell Quiescence

A limiting factor in the regenerative capacity of the adult brain is the abundance and proliferative ability of neural stem cells (NSCs). Adult NSCs are derived from a subpopulation of embryonic NSCs that temporarily enter quiescence during mid-gestation and remain quiescent until postnatal reactivation. Here we present evidence that the mechanistic/mammalian target of rapamycin (mTOR) pathway regulates quiescence entry in embryonic NSCs of the developing forebrain. Throughout embryogenesis, two downstream effectors of mTOR, p-4EBP1/2 T37/46 and p-S6 S240/244, were mutually exclusive in NSCs, rarely occurring in the same cell. While 4EBP1/2 was phosphorylated in stem cells undergoing mitosis at the ventricular surface, S6 was phosphorylated in more differentiated cells migrating away from the ventricle. Phosphorylation of 4EBP1/2, but not S6, was responsive to quiescence induction in cultured embryonic NSCs. Further, inhibition of p-4EBP1/2, but not p-S6, was sufficient to induce quiescence. Collectively, this work offers new insight into the regulation of quiescence entry in embryonic NSCs and, thereby, correct patterning of the adult brain. These data suggest unique biological functions of specific posttranslational modifications and indicate that the preferential inhibition of such modifications may be a useful therapeutic approach in neurodevelopmental diseases where NSC numbers, proliferation, and differentiation are altered.

developmental biology↗

bioRxiv2022

EV Fingerprinting: Resolving extracellular vesicle heterogeneity using multi-parametric flow cytometry

Mammalian cells release a heterogeneous array of extracellular vesicles (EVs) that impact human biology by contributing to intercellular communication. To resolve EV heterogeneity and define the EV populations associated with specific biological processes, we developed a method named "EV Fingerprinting" that discerns distinct vesicle populations using dimensional reduction of multi-parametric data collected by quantitative single-EV flow cytometry. After validating this method against synthetic standards, the EV Fingerprinting analysis of highly purified EVs enabled a much more granular resolution of biochemically distinct EV populations than previously established methods. The analysis of EVs produced after molecular perturbation of EV biogenesis through ablation of the GTPase Rab27a and overexpression of the tetraspanin CD63 revealed that EV Fingerprinting reflects the molecular state of a cell. Subsequent analysis of human plasma demonstrates the capacity of EV Fingerprinting to resolve EV populations in complex biological samples and detect tumor-cell derived EVs.

cell biology↗

bioRxiv2022

Alignment, Segmentation and Neighborhood Analysis in Cyclic Immunohistochemistry Data Using CASSATT

Cyclic immunohistochemistry (cycIHC) uses sequential rounds of colorimetric immunostaining and imaging for quantitative mapping of location and number of cells of interest. In addition, cycIHC benefits from the speed and simplicity of brightfield microscopy for data collection, making the collection of entire tissue sections and slides possible at a trivial cost compared to other high dimensional imaging modalities. However, large cycIHC datasets (greater than 50 GB) currently require an expert data scientist to concatenate separate open-source tools for each step of image pre-processing, registration, and segmentation, or the use of proprietary software. Here, we present a unified and user-friendly pipeline for processing, aligning, and analyzing cycIHC data - Cyclic Analysis of Single-Cell Subsets and Tissue Territories (CASSATT). CASSATT registers scanned slide images across all rounds of staining, segments individual nuclei, and measures marker expression on each detected cell. Beyond straightforward single cell data analysis outputs, CASSATT explores the spatial relationships between cell populations. By calculating the logodds of interaction frequencies between cell populations within tissues and tissue regions, this pipeline helps users identify populations of cells that interact - or do not interact - at frequencies that are greater than those occurring by chance. It also identifies specific neighborhoods of cells based on the assortment of neighboring cell types that surround each cell in the sample. The presence and location of these neighborhoods can be compared across slides or within distinct regions within a tissue. CASSATT was first tested using a newly generated cycIHC dataset consisting of six GBM tissue sections processed through eight cycles of AEC based IHC staining. Further validation was completed on a previously published lung cancer tissue microarray dataset consisting of 107 cores processed through eighteen cycles of staining and imaging. CASSATT is a fully open-source workflow tool developed to process cycIHC data and will allow greater utilization of this powerful staining technique.

bioinformatics↗

bioRxiv2022

Immune cell regulation in stem cell niche contacting glioblastomas.

Glioblastomas (GBM) are tumors for which immune-targeted therapies have failed to show clinical benefit and for which few biomarkers provide context for meaningful therapeutic stratification. Radiographic contact of GBM tumors with the lateral ventricle stem cell niche correlates with worse patient prognosis; however, the extent to which proximity to the ventricle impacts antitumor immunity remains unknown. We demonstrate that T cell checkpoint receptor expression is elevated in ventricle-contacting GBM as is the abundance of a specific, suppressive CD32+CD44+HLADhigh myeloid population suggesting a distinct immunoregulatory influence on antitumor immunity in proximity to the lateral ventricle. Phospho-specific mass cytometric profiling revealed extensively impaired immune signaling in ventricle-contacting GBM in response to inflammatory cytokine stimulation, further supporting a suppressive milieu influencing immunity at the lateral ventricle. Collectively, we identify a regulatory impact of ventricle contact on antitumor immunity in the brain, and reveal novel clinically targetable mechanisms of immunomodulation in patients with glioblastoma. Significance StatementWe demonstrate that the immune microenvironment of glioblastoma tumors contacting the lateral ventricle differs from non-contacting tumors. This work connects immune-biology to a radiographically detectable feature, the lateral ventricle, and highlights non-invasive imaging as a means to identify targetable immune features in glioblastoma tumors.

immunology↗

bioRxiv2021

STAT1 Gain-of-Function Variants Drive Altered T Cell Prevalence, Metabolism, and Heightened IL-6 Sensitivity

Patients with Signal Transducer and Activator of Transcription 1 (STAT1) gain-of-function (GOF) pathogenic variants exhibit susceptibility to infections, autoimmunity, and cancer due to enhanced or prolonged STAT1 phosphorylation following cytokine stimulation. While interferons (IFNs) are canonical STAT1 activators, other cytokines that may also contribute to pathology in STAT1 GOF patients have been less well defined. Here we analyzed the immune profiles and cytokine responses of two patients with heterozygous GOF mutations in the STAT1 coiled-coil domain. A systems immunology approach revealed major changes in the T cell compartment and minor changes in the B cells, NK cells, and myeloid cells. Both patients with STAT1 GOF differed from healthy individuals in the abundance and phenotype of effector memory, Th17, and Treg populations. STAT1 GOF T cells displayed a pattern of increased activation and had elevated markers of glycolysis and lipid oxidation. Hypersensitivity of T cells to IL-6 was observed with intense, sustained STAT1 phosphorylation in memory T cell populations that exceeded that induced by IFNs. Together, these results show a role for STAT1 in T cell metabolism and suggest that IL-6 may play a critical role to promote T cell memory formation and activation in patients with STAT1 GOF.

immunology↗

bioRxiv2021

Single-Cell Profiling of the Antigen-Specific Response to BNT162b2 SARS-CoV-2 RNA Vaccine

RNA-based vaccines against SARS-CoV-2 are critical to limiting COVID-19 severity and spread. Cellular mechanisms driving antigen-specific responses to these vaccines, however, remain uncertain. We used single-cell technologies to identify and characterized antigen-specific cells and antibody responses to the RNA vaccine BNT162b2 in longitudinal samples from a cohort of healthy donors. Mass cytometry and machine learning pinpointed a novel expanding, population of antigen-specific non-canonical memory CD4+ and CD8+ T cells. B cell sequencing suggested progression from IgM, with apparent cross-reactivity to endemic coronaviruses, to SARS-CoV-2-specific IgA and IgG memory B cells and plasmablasts. Responding lymphocyte populations correlated with eventual SARS-CoV-2 IgG and a donor lacking these cell populations failed to sustain SARS-CoV-2-specific antibodies and experienced breakthrough infection. These integrated proteomic and genomic platforms reveal an antigen-specific cellular basis of RNA vaccine-based immunity. ONE SENTENCE SUMMARYSingle-cell profiling reveals the cellular basis of the antigen-specific response to the BNT162b2 SARS-CoV-2 RNA vaccine.

immunology↗

bioRxiv2021

Nonclassical monocytes are prone to migrate into tumor in diffuse large B-cell lymphoma

Absolute count of circulating monocytes has been proposed as an independent prognostic factor in diffuse large B-cell lymphoma (DLBCL). However, monocyte nomenclature includes various subsets with pro-, anti-inflammatory, or suppressive functions, and their clinical relevance in DLBCL has been poorly explored. Herein, we broadly assessed circulating monocyte heterogeneity in 91 DLBCL patients. Classical- (cMO, CD14pos CD16neg) and intermediate- (iMO, CD14pos CD16pos) monocytes accumulated in DLBCL peripheral blood and exhibited an inflammatory phenotype. On the opposite, nonclassical monocytes (ncMO, CD14low CD16pos) were decreased in peripheral blood. Tumor-conditioned monocytes presented similarities with ncMO phenotype from DLBCL and were prone to migrate in response to CCL3, CCL5, and CXCL12, and presented similarities with DLBCL-infiltrated myeloid cells, as defined by mass cytometry. Finally, we demonstrated the adverse value of an accumulation of nonclassical monocytes in 2 independent cohorts of DLBCL. Key pointsO_LINonclassical monocytes are prone to migrate to DLBCL tumor C_LIO_LIHigh count of circulating nonclassical monocytes is an independent adverse event in DLBCL C_LI

immunology↗

bioRxiv2020

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead left out to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by [≥]95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.

systems biology↗

bioRxiv2020

Two new immature and dysfunctional neutrophil cell subsets define a predictive signature of sepsis useable in clinical practice

Sepsis is the leading cause of death in adult intensive care units. At present, sepsis diagnosis relies on non-specific clinical features. It could transform clinical care to have objective immune cell biomarkers that could predict sepsis diagnosis and guide treatment. For decades, neutrophil phenotypes have been studied in sepsis, but a diagnostic cell subset has yet to be identified. Here, high dimensional mass cytometry was used to reveal for the first time a specific neutrophil signature of sepsis severity that does not overlap with other inflammatory biomarkers, and that distinguishes patients with sepsis from those with non-infectious inflammatory syndrome. Unsupervised analysis of 42-dimesional mass cytometry data characterized previously unappreciated heterogeneity within the CD64+ immature neutrophils and revealed two new subsets distinguished by CD123 and PD-L1 expression. These immature neutrophils exhibited diminished activation and phagocytosis functions. The proportion of CD123-expressing neutrophils also correlated with clinical severity. Critically, this study showed that these two new neutrophil subsets were specific to sepsis and detectable by routine flow cytometry using seven markers. The demonstration here that a simple blood test distinguishes sepsis from other inflammatory conditions represents a key biological milestone that can be immediately translated into improvements in patient care. One Sentence SummaryCD123+ and/or PD-L1+ immature and dysfunctional neutrophil subsets identified by mass cytometry, define an early human blood signature of sepsis

immunology↗

bioRxiv2019

High risk glioblastoma cells revealed by machine learning and single cell signaling profiles

Recent developments in machine learning implemented dimensionality reduction and clustering tools to classify the cellular composition of patient-derived tissue in multi-dimensional, single cell studies. Current approaches, however, require prior knowledge of either categorical clinical outcomes or cell type identities. These algorithms are not well suited for application in tumor biology, where clinical outcomes can be continuous and censored and cell identities may be novel and plastic. Risk Assessment Population IDentification (RAPID) is an unsupervised, machine learning algorithm that identifies single cell phenotypes and assesses clinical risk stratification as a continuous variable. Single cell mass cytometry evaluated 34 different phospho-proteins, transcription factors, and cell identity proteins in tumor tissue resected from patients bearing IDH wild-type glioblastomas. RAPID identified and characterized multiple biologically distinct tumor cell subsets that independently and continuously stratified patient outcome. RAPID is broadly applicable for single cell studies where atypical cancer and immune cells may drive disease biology and treatment responses.

cancer biology↗