Biology subjects

Gaglia, G.

Publications and source records attributed to Gaglia, G..

9 recordsLinked to original sources

bioRxiv2025

Single-cell neural network classifiers reveal that PM21 NK cell expansion is dependent on B cell signaling

In the field drug development ML/AI methods are being applied to improve drug production speed, costs, and reliability. In allogenic NK cell therapy production, one of the biggest challenges is the inherent variability in the donors that provide the starting material for NK cell expansion. In this study we performed PM21-mediated NK cell expansion on 26 donors, and in parallel performed single-cell transcriptomics on the same donor sample prior to expansion. Canonical differential expression analysis and cell state abundance did not highlight any significant difference between donors with high and low NK cell expansion yield. Instead, training neural networks classifiers for high-yield donors enabled identifying several highly predictive models with perfect cross-validation recall. Further investigation of the most predictive models unveiled a previously unknown role for B cell in supportive NK cell expansion. Overall, this study represents a blueprint for combining deep phenotyping and machine learning methods to unveil novel biology and improve the quality and speed of delivery of cell therapeutics to patients.

systems biology↗

bioRxiv2025

AAV-mediated ARSA replacement for the treatment of Metachromatic Leukodystrophy

Metachromatic leukodystrophy (MLD) is an autosomal recessive neurodegenerative disorder caused by mutations in the arylsulfatase A (ARSA) gene, resulting in lower sulfatase activity and the toxic accumulation of sulfatides in the central and peripheral nervous system. Children account for 70% of cases and become progressively disabled with death occurring within 10 years of disease onset. Gene therapy approaches to restore ARSA expression via adeno-associated viral vectors (AAV) have been promising but hampered by limited brain biodistribution. We report the development of a novel capsid AAV.GMU01, demonstrating superior biodistribution and transgene expression in the central nervous system of non-human primates (NHPs). Next, we show that AAV.GMU01-ARSA treated MLD mice exhibit persistent, normal levels of sulfatase activity and a concomitant reduction in toxic sulfatides. Treated mice also show a reduction in MLD-associated pathology and auditory dysfunction. Lastly, we demonstrate that treatment with AAV.GMU01-ARSA in NHPs is well-tolerated and results in potentially therapeutic ARSA expression in the brain. In summary, we propose AAV.GMU01-ARSA mediated gene replacement as a clinically viable approach to achieve broad and therapeutic levels of ARSA.

neuroscience↗

bioRxiv2024

Elovl1 inhibition reduced very long chain fatty acids in a mouse model of adrenoleukodystrophy

Adrenoleukodystrophy (ALD) is a rare neurometabolic disease caused by mutations in the ABCD1 gene, which encodes for the peroxisomal very long chain fatty acid (VLCFAs) transporter. It is a debilitating disorder, which has a spectrum of clinical presentations. The most severe form is a rapidly progressing demyelinating disease called cerebral ALD or CALD. Patients with cALD have a life expectancy of 2-4 years after onset and symptoms often manifest in childhood. The other forms are adrenomyeloneuropathy or AMN, which is a slower progressing degeneration of the spinal cord, and adrenal insufficiency (Addison disease). Since the accumulation of VLCFAs are a common factor in all ALD pathologies, we identified therapeutic approach that could correct this metabolic defect. We developed a substrate reduction therapy (SRT) for ALD in the form of an inhibitor of the lipid elongase principally responsible for the generation of VLCFAs, Elovl1. This small molecule was able to successfully reduce the accumulation of VLCFA in the brain and spinal cord of ABCD1-/y mice. We used single nuclei RNA seq to identify the pathways altered in the ABCD1-/y mouse and corrected with Elovl1 inhibition. Though many lipid metabolism genes and pathways were indeed corrected, treatment with the Elovl1 inhibitor unexpectedly led to profound transcriptional changes beyond correction of pathways altered by loss of ABCD1. These data suggest that Elovl1 inhibition may have broader consequences in ABCD1-/y mice than correction of lipid homeostasis.

pharmacology and toxicology↗

bioRxiv2024

Glial state changes and neuroinflammatory RIPK1 signaling are a key feature of ALS pathogenesis

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that causes motor neuron loss in the brain and spinal cord. Neuroinflammation driven by activated microglia and astrocytes is prominent in ALS, but an understanding of cell state dynamics and which pathways contribute to the disease remains unclear. Single nucleus RNA sequencing of ALS spinal cords demonstrated striking changes in glial cell states, including increased expression of inflammatory and glial activation markers. Many of these signals converged on RIPK1 and the necroptotic cell death pathway. Activation of the necroptosis pathway in ALS spinal cords was confirmed in a large bulk RNA sequencing dataset and at the protein level. Blocking RIPK1 kinase activity delayed symptom onset and motor impairment and modulated glial responses in SOD1G93A mice. We used a human iPSC-derived motor neuron, astrocyte, and microglia tri-culture system to identify potential biomarkers secreted upon RIPK1 activation, inhibited pharmacologically in vitro, and modulated in the CSF of people with ALS treated with a RIPK1 inhibitor. These data reveal ALS-enriched glial populations associated with inflammation and suggest a deleterious role for neuroinflammatory signaling in ALS pathogenesis.

neuroscience↗

bioRxiv2023

Comparative Analysis of Association Networks Using Single-Cell RNA Sequencing Data Reveals Perturbation-Relevant Gene Signatures

Single-cell RNA sequencing (scRNA-seq) data has elevated our understanding of systemic perturbations to organismal physiology at the individual cell level. However, despite the rich information content of scRNA-seq data, the relevance of genes to a perturbation is still commonly assessed through differential expression analysis. This approach provides a one-dimensional perspective of the transcriptomic landscape, risking the oversight of tightly controlled genes characterized by modest changes in expression but with profound downstream effects. We present GENIX (Gene Expression Network Importance eXamination), a novel platform for constructing gene association networks, equipped with an innovative network-based comparative model to uncover condition-relevant genes. To demonstrate the effectiveness of GENIX, we analyze influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) collected from recovered COVID-19 patients, shedding light on the mechanistic underpinnings of gender differences. Our methodology offers a promising avenue to identify genes relevant to perturbation responses in biological systems, expanding the scope of response signature discovery beyond differential gene expression analysis. HIGHLIGHTSO_LIConventional methods used to identify perturbation-relevant genes in scRNA-seq data rely on differential expression analysis, susceptible to overlooking essential genes. C_LIO_LIGENIX leverages cell-type-specific inferred gene association networks to identify condition-relevant genes and gene programs, irrespective of their specific expression alterations. C_LIO_LIGENIX provides insight into the gene-regulatory response to the influenza vaccine in naive and recovered COVID-19 patients, expanding on previously observed gender-specific differences. C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/556872v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1837d3eorg.highwire.dtl.DTLVardef@1937860org.highwire.dtl.DTLVardef@c40114org.highwire.dtl.DTLVardef@22d3b9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma

Lymphocytes play a key role in immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. Here, we used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of tumor-bearing lung tissues from a Kras/p53 (KP) mouse model and human resections. Networks of directly interacting lymphocytes ( lymphonets) emerge as a distinctive feature of the anti-cancer immune response. Lymphonets nucleate from small T-cell clusters and incorporate B cells with increasing size. CXCR3-mediated trafficking modulates lymphonet size and number, but neoantigen expression directs intratumoral localization. Lymphonets preferentially harbor TCF1+/PD1+ progenitor CD8 T cells involved in responses to immune checkpoint blockade (ICB). Upon treatment of mice with ICB therapy or a neoantigen-targeted vaccine, lymphonets retain progenitor and gain cytotoxic CD8 T-cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of CD8 T-cell anti-tumor responses.

cancer biology↗

bioRxiv2022

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

New highly-multiplexed imaging technologies have enabled the study of tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis, as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood cell networks and immune-tumor interactions.

cell biology↗

bioRxiv2021

Temporal and spatial topography of cell proliferation in cancer

Proliferation is a fundamental trait of cancer cells but is poorly characterized in tumors by classical histologic methods. We use multiplexed tissue imaging to quantify the abundance of multiple cell cycle regulating proteins at single-cell level and develop robust multivariate proliferation metrics. Across cancers, the proliferative architecture is organized at two distinct spatial scales: large domains, and local niches enriched for specific immune lineages. A subset of tumor cells express cell cycle regulators in canonical patterns consistent with unrestrained proliferation, a phenomenon we refer to as "cell cycle coherence". By contrast, the cell cycles of other tumor cell populations are skewed toward a specific phase or characterized by non-canonical (incoherent) marker combinations. Coherence varies across space, with changes in oncogene activity, and with therapeutic intervention, and is associated with aggressive behavior. Multivariate measures capture clinically significant features of cancer proliferation, a fundamental step in enabling more precise use of anti-cancer therapies.

systems biology↗

bioRxiv2019

Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer

In this data descriptor, we document a dataset of multiplexed immunofluorescence images and derived single-cell measurements of immune lineage and other markers in formaldehyde-fixed and paraffin-embedded (FFPE) human tonsil and lung cancer tissue. We used tissue cyclic immunofluorescence (t-CyCIF) to generate fluorescence images which we artifact corrected using the BaSiC tool, stitched and registered using the ASHLAR algorithm, and segmented using ilastik software and MATLAB. We extracted single-cell features from these images using HistoCAT software. The resulting dataset can be visualized using image browsers and analyzed using high-dimensional, single-cell methods. This dataset is a valuable resource for biological discovery of the immune system in normal and diseased states as well as for the development of multiplexed image analysis and viewing tools.\n\nMETADATA SUMMARY\n\nO_TBL View this table:\norg.highwire.dtl.DTLVardef@6edf3eorg.highwire.dtl.DTLVardef@1026e07org.highwire.dtl.DTLVardef@85a7baorg.highwire.dtl.DTLVardef@c6d7e5org.highwire.dtl.DTLVardef@87fccf_HPS_FORMAT_FIGEXP M_TBL C_TBL

cancer biology↗