Biology subjects

Milo, I.

Publications and source records attributed to Milo, I..

7 recordsLinked to original sources

bioRxiv2026

Spatially enforced engagement of PD-1⁺ T cells with AXL⁺ cells reprograms the tumor microenvironment and overcomes anti-PD-1 resistance

Background: AXL overexpression promotes tumor-cell plasticity, local immune suppression, and resistance to PD-1 blockade. Conventional anti-AXL and anti-PD-1 antibodies combinations act independently and do not ensure physical proximity between PD-1+ T cells and AXL+ cells within the immunosuppressive tumor microenvironment. Methods: To address this limitation, we developed a non-tandem, tetravalent IgG bispecific antibody designed to simultaneously engage PD-1 and AXL (BiTE-AXL/PD-1). Its antitumor activity was evaluated using in vitro functional assays and humanized and immunocompetent head and neck squamous cell carcinoma (HNSCC) models and compared with the combination of monospecific anti-AXL and anti-PD-1 antibodies. Spatial multiplex imaging was used to characterize treatment-associated changes in the tumor microenvironment of an anti-PD-1-resistant murine model. Results: BiTE-AXL/PD-1 enhanced T-cell activation, promoted AXL-dependent tumor-T-cell conjugation, and increased tumor-cell killing in vitro, outperforming the combination of the two monospecific antibodies. In humanized and immunocompetent HNSCC models, BiTE treatment significantly delayed tumor growth compared with the antibody combination and was well tolerated, with no overt toxicity. Spatial multiplex imaging revealed a shift from a fibroblast-dominated microenvironment characterized by exhausted CD8+ T cells toward dendritic-cell- and CD8+ T-cell-enriched niches associated with antitumor immune activity. Conclusion: BiTE-AXL/PD-1 promotes physical proximity between PD-1+ T cells and AXL+ cells, enhances antitumor immune responses, and remodels the immunosuppressive tumor microenvironment. These findings support PD-1/AXL bispecific targeting as a promising therapeutic strategy to overcome resistance to PD-1 blockade in HNSCC.

cancer biology↗

bioRxiv2025

Clonal memory of cell division in humans diverges between healthy haematopoiesis and acute myeloid leukaemia

Clonal memory, a cellular property inherited across at least two divisions, has emerged as a key driver of cell heterogeneity. To uncover its roles in human haematopoiesis, we developed high-resolution ex vivo tools that track both division and fate commitment of individual primary human haematopoietic stem and progenitor cells (HSPCs). We show that human HSPCs display a clonal memory of division, as cells descending from the same ancestor cell divide synchronously over multiple generations. In parallel, HSPCs inherit a clonal memory of fate commitment, independently of lineage identity. Both forms of clonal memory persist over at least two divisions, across different HSPC commitment stages and cell culture conditions. In contrast, malignant haematopoiesis exhibits lower synchronicity, revealing a disruption of clonal memory in leukemic cells. Epigenetic remodelling using a bromodomain inhibitor partially restores the clonal memory in division in leukemic HSPCs, highlighting the plasticity of this trait and its potential for therapeutic modulation. Our findings position clonal memory as a key regulator of human haematopoietic stem cell behaviour. Demonstrating that clonal memory can be modulated opens new avenues for tuning cell heterogeneity in healthy and pathological tissues.

cell biology↗

bioRxiv2025

CellTune: An integrative software for accurate cell classification in spatial proteomics

Spatial proteomics measures multiple proteins in situ, capturing tissue complexity. However, cell classification in densely packed tissues remains challenging due to the lack of efficient classification algorithms, annotation tools, and high-quality labeled datasets to benchmark computational methods. We introduce CellTune, an integrated software for analysis of large spatial proteomics datasets, which streamlines precise cell classification through an optimized human-in-the-loop active learning workflow. It advances core capabilities across within a unified, intuitive, and code-free interface. To evaluate CellTune, we created CellTuneDepot, a resource of 40k manually-annotated cells and 3.5 million high-quality labeled cells across 60 cell types. CellTune outperforms alternative methods, achieving accuracy comparable to human performance while enabling increased classification resolution and discovery of novel cell types. Together, CellTune and CellTuneDepot provide researchers with a tool for state-of-the-art classification accuracy and resolution at scale to drive biological insights.

bioinformatics↗

bioRxiv2024

Immune organization in sentinel lymph nodes of melanoma patients is prognostic of distant metastases

Sentinel lymph node (sLN) biopsy is part of melanoma staging, as involved LNs indicate a higher risk of recurrence. However, how the sLN is shaped by the tumor and reciprocally affects metastatic progression is poorly understood. Here, we mapped immune organization in involved and non-involved sLNs of 69 melanoma patients using high-resolution spatial proteomics, spatial transcriptomics and deep learning, leveraging the data for prognostic evaluation. In patients with involved LNs, a robust T cell response correlated with absence of recurrence. In non-involved LNs, protection from metastases was linked to expanded sinuses colocalized with plasmablasts whereas CCR7+ cells and Tregs correlated with future development of distant metastases. We trained a model that predicts development of distant metastases with 79% and 93% AUC for non-metastatic and metastatic LNs, respectively. Our findings reveal conserved immune patterns in sLNs that prospectively identify patients at risk for metastatic disease and may aid in therapeutic decisions.

cancer biology↗

bioRxiv2024

A spatial atlas of human gastro-intestinal acute GVHD reveals epithelial and immune dynamics underlying disease pathophysiology

Acute graft-versus-host disease (aGVHD) is a significant complication of allogeneic hematopoietic stem cell transplantation (aHSCT), driven by alloreactive donor T cells in the gut. However, the roles of additional donor and host cells in this process are not fully understood. We conducted multiplexed imaging on 59 biopsies from patients with gastrointestinal GVHD and 10 healthy controls, revealing key pathological changes, including fibrosis, crypt alterations, loss of Paneth cells, accumulation of endocrine cells, and disrupted immune organization, particularly a reduction in IgA-secreting plasma cells. Interestingly, CD8T cells were enriched only in a subset of patients, while others exhibited non-canonical enrichments of macrophages and neutrophils. Post-transplantation time significantly influenced immune composition, with host cells dominating plasma and T cell compartments long after transplantation. This spatial atlas of healthy duodenum and GVHD uncovers non-canonical immune dynamics, offering insights into disease pathophysiology and potential clinical applications in GVHD and other inflammatory bowel diseases.

immunology↗

bioRxiv2023

Escalating High-dimensional Imaging using Combinatorial Channel Multiplexing and Deep Learning

Understanding tissue structure and function requires tools that quantify the expression of multiple proteins at single-cell resolution while preserving spatial information. Current imaging technologies use a separate channel for each individual protein, inherently limiting their throughput and scalability. Here, we present CombPlex (COMBinatorial multiPLEXing), a combinatorial staining platform coupled with an algorithmic framework to exponentially increase the number of proteins that can be measured from C up to 2c - 1. In CombPlex, every protein can be imaged in several channels, and every channel contains agglomerated images of several proteins. These combinatorically-compressed images are then decompressed to individual protein-images using deep learning. We achieve accurate reconstruction when compressing the stains of twenty-two proteins to five imaging channels and demonstrate that the approach works in both fluorescence microscopy and in mass-based imaging. Combinatorial staining coupled with deep-learning decompression can escalate the number of proteins measured using any imaging modality, without the need for specialized instrumentation. Coupling CombPlex with instruments for high-dimensional imaging could pave the way to image hundreds of proteins at single-cell resolution in intact tissue sections.

systems biology↗

bioRxiv2022

CellSighter - A neural network to classify cells in highly multiplexed images

Multiplexed imaging enables measurement of multiple proteins in situ, offering an unprecedented opportunity to chart various cell types and states in tissues. However, cell classification, the task of identifying the type of individual cells, remains challenging, labor-intensive, and limiting to throughput. Here, we present CellSighter, a deep-learning based pipeline to accelerate cell classification in multiplexed images. Given a small training set of expert-labeled images, CellSighter outputs the label probabilities for all cells in new images. CellSighter achieves over 80% accuracy for major cell types across imaging platforms, which approaches inter-observer concordance. Ablation studies and simulations show that CellSighter is able to generalize its training data and learn features of protein expression levels, as well as spatial features such as subcellular expression patterns. CellSighters design reduces overfitting, and it can be trained with only thousands or even hundreds of labeled examples. CellSighter also outputs a prediction confidence, allowing downstream experts control over the results. Altogether, CellSighter drastically reduces hands-on time for cell classification in multiplexed images, while improving accuracy and consistency across datasets.

systems biology↗