Biology subjects

Lee, M. E.

Publications and source records attributed to Lee, M. E..

7 recordsLinked to original sources

bioRxiv2026

Limits of marker information transfer]{Limits of marker information transfer in cross-panel cytometry imputation

Marker imputation can bridge cytometry datasets acquired with different panels and platforms. Its validity depends on whether common markers retain information about unmeasured target markers and whether those relationships transfer across assay contexts. We evaluated these limits in two paired flow cytometry-CyTOF cohorts comprising 134 patients and 257 specimens. A patient-held-out framework distinguished within-platform same-cell reconstruction from cross-platform distribution translation without cell correspondence. We compared conventional machine-learning and deep generative estimators with median imputation and used permutation controls to estimate the contributions of patient matching and source-exclusive markers. Evaluation covered marker values, specimen distributions, phenotype utility, and directional predictive relationships among markers. Within platforms, exact k-nearest neighbors reduced mean absolute error by 12.8-35.0% relative to median imputation. However, the median-based reconstructions were contracted, with a median ratio of predicted to observed interquartile range below 1 in every context and evaluable population. Patient matching improved cross-platform distribution recovery, whereas adding source-exclusive markers or higher-capacity estimators did not consistently produce net gains. In the independently annotated acute myeloid leukemia (AML) cohort, reconstructed markers largely preserved broad-lineage classification but did not recover fine-phenotype information available through direct measurement. In AML-CyTOF, the measured full panel outperformed the reconstructed representation by 10.25 percentage points for fine phenotypes. Predictive relationships among markers were context-specific. Most targets were better explained by several markers than by one. Marker selection based on these relationships showed a trade-off between panel size and pairwise coverage. Paired samples and imputation can therefore connect selected marker distributions across panels but should not be treated as substitutes for direct measurement. Cross-panel cytometry should be evaluated with patient-held-out tests matched to the resolution of available ground truth and the intended downstream use.

bioinformatics↗

bioRxiv2026

Decomposing scanner batch effects and their impact on pathology foundation models

Pathology foundation models (PFMs) are expected to represent the same tissue in the same way on any scanner, but their embeddings still carry scanner signals. Scanner-related image differences and the scanner sensitivity of PFMs have each been described. How tissue-dependent changes in color and spatial frequency relate to shifts in PFM representations, and what this means for correction, is less understood. We imaged 103 slides from 37 normal tissue types on six scanners (PanNormal; 95,780 matched locations) and used the real paired images as targets. We used the public PLISM dataset for external validation. First, we decomposed scanner differences into image properties. Color and spatial frequency changed together, by amounts that depended on tissue type. Second, corrections that matched these measured properties brought images closer to the target more reliably than representations. Pix2Pix reduced the image residual by 69% but increased UNI distance to the target by 27%. The same frequency matching helped one scanner and hurt another. Probes still distinguished corrected from real target embeddings after every correction. Third, changing one frequency band at a time showed that PFMs differ in how strongly each band moves them and whether it moves them toward the target. Across eight PFMs pretrained on whole-slide image tiles, the models that moved least under these small changes were the most robust to real scanner differences (Spearman {rho}=-0.90). Aligning images and aligning representations are therefore different goals. The gap between them depends on scanner, tissue, and model.

bioinformatics↗

bioRxiv2026

CytoGate-Bench: an LLM benchmark for cross-panel cell gating in cytometry

In cytometry, the workhorse single-cell technology of clinical immunology, every study defines its own antibody panel and cell-type vocabulary, so a classifier trained on one cannot annotate the next. Immunologists instead annotate by manual gating, splitting one parent population at a time on a two-marker plot, down an expert-defined hierarchy. We introduce CytoGate-Bench, a benchmark that reformulates this per-step procedure as a zero-shot, panel-agnostic task for large language models. It comprises 23,646 expert-annotated instances re-curated from 11 public flow- and mass-cytometry cohorts spanning eight marker panels. Across six open- and closed-weight backbones, the strongest formulation draws one rectangular gate per candidate and falls within the range of trained, panel-specialized baselines. It degrades less under distribution shift. Walking the hierarchy stepwise outperforms predicting every cell type at once. Ablations trace the signal to the data distribution shape and curated marker priors. However, adding vision or a self-verification loop systematically tightens gates.

bioinformatics↗

bioRxiv2026

Deep learning representations of human Immune Health for precision immunology

The human immune system is composed of [~]30-50 distinct cell types, each of which can exist in different states of activation or differentiation. Indeed, the mammalian immune system has evolved to sense and respond to infections, cancers, injuries, and changes in tissue or host homeostasis (1). Moreover, an increasingly large fraction of approved drugs target the immune system directly, and/or cause immune changes (2-4). A key feature of the immune system is to store some of this information, for example as innate or adaptive immune memory (5). In addition, rewiring of immune network architecture induced by disease, environmental exposures, drug treatments, and/or chronological age allows the immune system to store information in the pattern of connections and activity across populations of immune cells. This ensemble information storage, in addition to changes to individual cells, functions as a major way the immune system encodes aspects of immune history and future potential. Genetic information can identify inherited risk alleles, but cannot capture the continual remodeling of the immune system shaped by exposures, infection, inflammation, therapy, and aging (6, 7). To define and use such ensemble immunotypes, we developed a self-supervised deep learning framework that transforms high-dimensional immune profiles into representations of immune health. MAESTRO (MAsked Encoding Set TRansformer with self-distillatiOn) encodes a set of cells from an individual into an embedding that captures immune cell population-level organization. Pretrained on 1,792 peripheral blood samples comprising over 418 million immune cells across 13 clinical diagnoses, MAESTRO learns immune fingerprints that are stable within individuals yet diverse across populations, states of health, disease, and treatment, providing a quantitative basis for comparing immune states across individuals and over time. These fingerprints capture immune architecture beyond coarse cell type proportions, enabling patient-efficient clinical prediction using simple task specific models. MAESTRO model embeddings retain a temporal dimension of immune history and potential, reflecting signatures of past exposures and baseline features that predict future immune responses. Finally, we demonstrate a translational precision immunotherapy application by testing this approach in metastatic Pancreatic Ductal Adenocarcinoma (PDAC), where pretreatment immune landscape circuitry maps enable patient stratification and therapeutic response prediction. Overall, we developed a large, attention-based model that captures deep network architecture of immune states through self- supervised representations of immune cytometry data as a reusable foundation for precision immunology, converting immune complexity into clinically actionable embeddings for diagnosis, monitoring, and therapy selection.

immunology↗

bioRxiv2025

Weak SLP-76-PLC-γ1 interaction in the LAT-nucleated multi-protein complex fine-tunes TCR signal strength to optimize T cell responsiveness

Upon TCR engagement several protein tyrosine kinases are recruited and activated, and adapter proteins and enzymes are phosphorylated on tyrosine residues, leading to further events characterizing activated T cells. Phosphorylation of the LAT adapter protein enables binding of the enzyme PLC-{gamma}1 and of a dimer of two additional adapter proteins Gads and SLP-76, forming a tetrameric structure. Within this heterotetramer there is a weak interaction between SLP-76 and PLC-{gamma}1, and the relevant binding sites of SLP-76 and PLC-{gamma}1 are highly conserved in vertebrates. To address the biological relevance of this weak interaction, we introduced a mutation in the SLP-76 that enhanced its affinity for PLC-{gamma}1 and found that this mutation increased PLC-{gamma}1 activity and altered thymocyte development and peripheral T cell responses due to enhanced TCR signal strength. The conserved weak SLP-76-PLC-{gamma}1 interaction is critical for the controlled activation of PLC-{gamma}1, thus fine-tuning TCR signal strength to optimize T cell-mediated immunity.

immunology↗

bioRxiv2022

Generation of anti-tumor chimeric antigen receptors incorporating T cell signaling motifs

Chimeric antigen receptors (CAR) T cells have been successfully used to treat lymphoma, leukemia, and multiple myeloma, but adverse effects due to cytokine secretion, CAR-T cell exhaustion, and loss of target antigen have limited their potential. Furthermore, while CARs have been designed to harness T Cell Receptor (TCR) signaling, they are significantly less sensitive than TCRs, resulting in suboptimal signaling. We have developed novel Chimeric Adapter Proteins (CAPs) that are designed to trigger signaling downstream of the TCR{zeta} chain. CAPs are chimeric molecules that contain adapter domains in tandem with the kinase domain of ZAP70, fused to an extracellular targeting domain. We hypothesized that CAPs would be more potent than CARs because kinetic proofreading steps that define the signaling threshold and the inhibitory regulation of upstream molecules are bypassed. Indeed, second generation CAPs exhibited high anti-tumor efficacy, and significantly enhanced long-term in vivo tumor clearance in leukemia-bearing NSG mice as compared with conventional CD19-28{zeta} CAR-T. Mechanistically, CAPs were activated in an Lck-independent manner and displayed slower phosphorylation kinetics and a longer duration of signaling compared with 28{zeta}-CAR. The unique signaling properties of CAPs may therefore be harnessed to improve the in vivo efficacy of T cells engineered to express an anti-tumor chimeric receptor.

immunology↗

bioRxiv2021

COVID-19 Mortality is Associated with Impaired Innate Immunity in Pre-existing Health Conditions

BackgroundCOVID-19 poses a life-threatening endangerment to individuals with chronic diseases. However, not all comorbidities affect COVID-19 prognosis equally. Some increase the risk of COVID-19 related death by more than six folds while others show little to no impact. To prevent severe outcomes, it is critical that we comprehend pre-existing molecular abnormalities in common health conditions that predispose patients to poor prognoses. In this study, we aim to discover some of these molecular risk factors by associating gene expression dysregulations in common health conditions with COVID-19 mortality rates in different cohorts. MethodsWe focused on fourteen pre-existing health conditions, for which age-and-sex-adjusted hazard ratios of COVID-19 mortality have been documented. For each health condition, we analyzed existing transcriptomics data to identify differentially expressed genes (DEGs) between affected individuals and unaffected individuals. We then tested if fold changes of any DEG in these pre-existing conditions were correlated with hazard ratios of COVID-19 mortality to discover molecular risk factors. We performed gene set enrichment analysis to identify functional groups overrepresented in these risk factor genes and examined their relationships with the COVID-19 disease pathway. ResultsWe found that upregulated expression of 70 genes and downregulated expression of 181 genes in pre-existing health conditions were correlated with increased risk of COVID-19 related death. These genes were significantly enriched with endoplasmic reticulum (ER) function, proinflammatory reaction, and interferon production that participate in viral transcription and immune responses to viral infections. ConclusionsImpaired innate immunity in pre-existing health conditions are associated with increased hazard of COVID-19 mortality. The discovered molecular risk factors are potential prognostic biomarkers and targets for therapeutic interventions.

bioinformatics↗