Biology subjects

Harris, C. R.

Publications and source records attributed to Harris, C. R..

8 recordsLinked to original sources

bioRxiv2026

TileBac: A Benchmark CryoEM Dataset of Bacteria in Ultralow-Dose Montage Tiles

Current segmentation models are capable of routine identification of biological features in noisy cryogenic electron microscopy (cryoEM) images. However, there are still challenges with complete segmentation of high boundary, thin objects such as bacterial cell envelopes and flagella. Moreover, ultralow-dose cryoEM images pose as an additional challenge to boundary distinctions between the object and background. Here, we present TileBac, a benchmark dataset of ultralow-dose montage tiles of Pantoea sp. YR343 to segment bacterial inner and outer membranes for evaluation of model effectiveness. We show that foundation models outperform convolutional neural networks at continuous bacterial cell envelope segmentation despite having lower performance metrics. We release the TileBac benchmark dataset on Hugging Face for further insights into model architecture development.

microbiology↗

bioRxiv2026

Top Model Decision Tree: Selecting Segmentation Models for Reliable Quantitative Analysis in Low- and Ultralow-Dose CryoEM

Deep learning neural networks provide a powerful approach for segmenting low-contrast cryogenic electron microscopy (cryoEM) images. However, model performance can vary significantly across imaging conditions and may hinder downstream quantitative analyses. Here, we present a structured evaluation workflow to systematically screen segmentation models based on performance, inference speed, robustness across imaging conditions, and reliability of downstream quantitative measurements. Using the Bacterial Cell Envelope Thickness Tool (BCET) as a test case, we evaluate multiple architectures (YOLOv11, YOLO26, U-Net, Detectron2, and SAM3) under low-dose and ultralow-dose cryoEM conditions. While several models achieve high metrics, model choice strongly influences downstream measurements of envelope thickness. Models optimized for high F1-scores may produce unreliable segmentation masks from object crowding, interpolation artifacts or imaging conditions. Our results reveal distinct trade-offs between performance, speed, and robustness amongst models. YOLOv11 provides the highest fidelity membrane segmentation for quantitative measurements and the Meta-based model SAM3 offers improved robustness under ultralow-dose conditions with competitive inference performance. This work provides practical guidance for model selection in cryoEM workflows, emphasizing that optimal choice depends on experimental priorities and downstream analysis requirements rather than metrics alone. These findings are broadly relevant to cryoEM workflows as AI-based analysis expands beyond the biological sciences. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/730486v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@f29df4org.highwire.dtl.DTLVardef@601d6eorg.highwire.dtl.DTLVardef@2c5023org.highwire.dtl.DTLVardef@1413f76_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures

Bacteria adapt by undergoing dynamic structural changes in response to environmental cues, which are often indicative of deeper phenotypic shifts in physiology and behavior. Understanding these changes across length scales is crucial for elucidating bacterial lifecycles, informing antifouling surface design, enhancing pathogen detection, and advancing renewable energy applications. Cryogenic electron microscopy (cryoEM) enables high-resolution imaging of bacterial structures in hydrated, biologically relevant conditions. However, current quantitative analysis strategies that extract structural information from bacterial samples remain labor-intensive. This work presents an AI-driven segmentation workflow tailored to low-dose cryoEM datasets to rapidly analyze bacterial ultrastructural features from Pantoea sp. YR343, a Gram-negative bacterium isolated from the rhizosphere of Populus deltoides that forms robust biofilms along plant roots. YOLOv11 image segmentation quantifies inner and outer membrane thickness and flagella length, enabling automated analysis of bacterial ultrastructure. The workflow reliably distinguishes membranes from carbon edges of the TEM grid and contaminant crystalline ice while matching manual measurements and increasing throughput. Flagella detection routines additionally quantify nearest-neighbor proximity between bacterial envelopes and flagella. A field-of-view module further detects bacteria at low magnification for rapid screening. Together, these tools provide a scalable and automated framework for high-throughput, quantitative analysis of bacterial ultrastructure in cryoEM data. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=39 SRC="FIGDIR/small/692658v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@18eac2org.highwire.dtl.DTLVardef@1dc67a1org.highwire.dtl.DTLVardef@1179499org.highwire.dtl.DTLVardef@11d08f8_HPS_FORMAT_FIGEXP M_FIG AI-based tools enable rapid characterization of bacterial ultrastructure in low-dose cryoEM. The envelope thickness tool quantifies membrane thickness and anisotropy. The flagella module analyzes filament morphology and detects cell-flagella contacts. The field-of-view (FOV) module C_FIG

microbiology↗

bioRxiv2024

The circadian gene Dec2 promotes pancreatic cancer dormancy by regulating tumor cell antigen presentation to facilitate immune evasion

The mechanisms that regulate cancer dormancy remain poorly understood. Using a mouse model of resectable pancreatic adenocarcinoma (PDAC), we identified Dec2 as a gene that was upregulated in metastatic dormant tumor cells. Deletion of Dec2 from tumor cells substantially increased mouse survival after resection due to an immune-mediated mechanism as the survival benefit was abrogated in immunodeficient conditions. Dec2 promoted immune evasion by repressing multiple components of the MHC-I dependent antigen presentation pathway in tumor cells. Dec2 is a regulator of circadian rhythms, and we found several components of the antigen presentation pathway oscillated in a circadian manner that was lost upon deletion of Dec2. Moreover, T-cell mediated tumor cell killing varied depending on the time of day. We suggest that lowered MHC-I presentation of antigens during rest phase is a natural effect of the circadian clock, which is exploited by Dec2-overexpressing pancreatic tumors to evade the immune system.

cancer biology↗

bioRxiv2024

Comparative metagenomics of tropical reef fishes show conserved core gut functions across hosts and diets with diet-related functional gene enrichments

Fish gut microbial communities are important for the breakdown and energy harvesting of the host diet. Microbes within the fish gut are selected by environmental and evolutionary factors. To understand how fish gut microbial communities are shaped by diet, three tropical fish species (hawkfish, Paracirrhites arcatus; yellow tang, Zebrasoma flavescens; and triggerfish, Rhinecanthus aculeatus) were fed piscivorous (fish meal pellets), herbivorous (seaweed), and invertivorous (shrimp) diets, respectively. From fecal samples, a total of 43 metagenome assembled genomes (MAGs) were recovered from all fish diet treatments. Each host-diet treatment harbored distinct microbial communities based on taxonomy, with Proteobacteria, Bacteroidota, and Firmicutes being the most represented. Based on their metagenomes, MAGs from all three host-diet treatments demonstrated a baseline ability to degrade proteinaceous, fatty acid, and simple carbohydrate inputs and carry out central carbon metabolism, lactate and formate fermentation, acetogenesis, nitrate respiration, and B vitamin synthesis. The herbivorous yellow tang harbored more functionally diverse MAGs with some complex polysaccharide degradation specialists, while the piscivorous hawkfishs MAGs were more specialized for the degradation of proteins. The invertivorous triggerfishs gut MAGs lacked many carbohydrate degrading capabilities, resulting in them being more specialized and functionally uniform. Across all treatments, several MAGs were able to participate in only individual steps of the degradation of complex polysaccharides, suggestive of microbial community networks that degrade complex inputs. ImportanceThe benefits of healthy microbiomes for vertebrate hosts include the breakdown of food into more readily usable forms and production of essential vitamins from their hosts diet. Compositions of microbial communities in the guts of fish in response to diet have been studied, but lack a comprehensive understanding of the genome-based metabolic capabilities of how they support their hosts. Therefore, we assembled genomes of several gut microbes collected from the feces of three fish species that were being fed different diets to illustrate how individual microbes can carry out specific steps in the degradation and energy utilization of various food inputs and support their host. Herbivorous fish harbored a functionally diverse microbes with plant matter degraders, while the piscivorous and invertivorous fish had microbes that were more specialized in protein degradation.

microbiology↗

bioRxiv2023

Consensus tissue domain detection in spatial -omics data using MILWRM

Spatially resolved molecular assays provide high dimensional genetic, transcriptomic, proteomic, and epigenetic information in situ and at various resolutions. Pairing these data across modalities with histological features enables powerful studies of tissue pathology in the context of an intact microenvironment and tissue structure. Increasing dimensions across molecular analytes and samples require new data science approaches to functionally annotate spatially resolved molecular data. A specific challenge is data-driven cross-sample domain detection that allows for analysis within and between consensus tissue compartments across high volumes of multiplex datasets stemming from tissue atlasing efforts. Here, we present MILWRM - multiplex image labeling with regional morphology - a Python package for rapid, multi-scale tissue domain detection and annotation. We demonstrate MILWRMs utility in identifying histologically distinct compartments in human colonic polyps and mouse brain slices through spatially-informed clustering in two different spatial data modalities. Additionally, we used tissue domains detected in human colonic polyps to elucidate molecular distinction between polyp subtypes. We also explored the ability of MILWRM to identify anatomical regions of mouse brain and their respective distinct molecular profiles.

systems biology↗

bioRxiv2021

Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images

MotivationThe multiplexed imaging domain is a nascent single-cell analysis field with a complex data structure susceptible to technical variability that disrupts inference. These in situ methods are valuable in understanding cell-cell interactions, but few standardized processing steps or normalization techniques of multiplexed imaging data are available. ResultsWe implement and compare data transformations and normalization algorithms in multiplexed imaging data. Our methods adapt the ComBat and functional data registration methods to remove slide effects in this domain, and we present an evaluation framework to compare the proposed approaches. We present clear slide-to-slide variation in the raw, unadjusted data, and show that many of the proposed normalization methods reduce this variation while preserving and improving the biological signal. Further, we find that dividing this data by its slide mean, and the functional data registration methods, perform the best under our proposed evaluation framework. In summary, this approach provides a foundation for better data quality and evaluation criteria in the multiplexed domain. Availability and ImplementationSource code is provided at https://github.com/statimagcoll/MultiplexedNormalization. Contactcoleman.r.harris@vanderbilt.edu Supplementary informationSupplementary information is available online.

bioinformatics↗

bioRxiv2020

A Novel Model of Pancreatic Cancer Dormancy Reveals Mechanistic Insights and a Dormancy Gene Signature with Human Relevance

Latent recurrences following curative-intent pancreatic cancer surgery is a major clinical problem thought to be due to the reactivation of dormant tumor cells that disseminate before the primary tumor has been removed. How dormancy is established and ultimately reversed to drive recurrence is poorly understood. Here we introduce a novel model of pancreatic cancer dormancy that mimics early and latent survival outcomes of resected patients. Using single-cell transcriptomics we compared primary, dormant, and reactivated tumor cells and found the primary and reactivated tumor cell transcriptomes clustered together with and away from the dormant tumor cells. Using a chromatin accessibility assay we found dormancy exhibits large scale changes in chromatin remodeling. Dormant tumor cells express cancer stem cell markers that are lost upon reactivation and are chemotherapy resistant. We identified a dormancy gene signature and investigated this in patients undergoing surgery for localized PC by isolating cells from the primary tumor and liver disseminated tumor cells (DTCs) for single-cell transcriptomics. We found the signature correlated with DTCs indicating that these cells are dormant at the time of surgery. The signature also identified CCL5 as a novel dormancy marker in PC. Mechanisms of PC dormancy include upregulation of the transcriptional repressor Dec2 which drives quiescence, monoallelic suppression of the mutant KRAS allele by DNA methylation, and immunoregulation. We conclude that PC dormancy is a highly plastic and heterogeneous cellular state governed by tumor cell autonomous and non-autonomous mechanisms. One Sentence SummaryA novel model of resectable pancreatic cancer reveals pancreatic cancer dormancy is characterized by significant cellular plasticity, heterogeneity and chromatin remodeling

cancer biology↗