Biology subjects

Rhodes, M.

Publications and source records attributed to Rhodes, M..

11 recordsLinked to original sources

bioRxiv2025

SPACE: spatially resolved multiomic analysis for high-throughput CRISPR screening in 3D models

Current spatial CRISPR screening technologies are limited by targeted readouts and high costs, restricting the scope of biological discovery. Here we present SPAtial Cell Exploration (SPACE), a spatial CRISPR screening platform that integrates whole-transcriptome profiling ([~]18,000 genes), multiplexed protein detection ([~]68 markers), and CRISPR perturbation mapping at subcellular resolution. SPACE significantly reduces whole-transcriptome profiling costs compared to sequencing methods while preserving spatial context. We demonstrate SPACE by screening 43 CRISPR knockouts (KOs) across [~]100,000 cells in hundreds of cancer-associated fibroblast (CAF)-tumor spheroids, obtaining whole-transcriptome and multiplexed protein readout from the same exact cells. SPACE revealed previously unknown regulatory mechanisms on tumor extracellular matrix (ECM) remodeling, and identified spatially-resolved ligand-receptor interactions and perturbation-specific spatial gene signatures that are not detectable with dissociation-based methods. This scalable, cost-effective platform provides a transformative framework for high-throughput spatial perturbation studies in complex tissue models.

cell biology↗

bioRxiv2025

Big Bones Mean Big Muscles: AI Quantifies 71 Individual Muscles Across the Whole Body, Revealing Widespread Links Between Muscle, Bone, and Body Size

Body sizes and shapes vary widely, even among healthy adults, resulting in diverse muscle sizes, strengths, and performance capacities. This study developed a novel AI-driven algorithm to segment and analyze individual muscles and bones from whole-body MRI. Validated via inter- and intra-observer analyses, the algorithm created 3D segmentations of 71 muscles and 13 bones across the upper limbs, trunk, and lower limbs in 48 healthy adults (24 males, 24 females) aged 18-49 years. Muscle volume, asymmetry, length, and fat fraction were quantified. While asymmetry and fat fraction varied across muscles, they did not differ significantly between sexes. Total muscle volume was the strongest predictor of individual muscle volume, followed by bone volume, which correlated with muscle size at whole-body, regional, and individual levels. Muscle-to-body size relationships (e.g., mass, height, BMI) differed between sexes, while bone-to-body size relationships did not. These findings suggest that skeletal size ("frame size") is a key determinant of muscularity. This study provides the most comprehensive in vivo dataset of human skeletal muscle to date, offering a multi-factorial explanation for variation in muscularity and benchmarks for applications ranging from athletic performance to clinical assessments in muscle disorders. SUMMARYUnderstanding how muscle size varies across the body and what influences these differences is key to advancing both health and performance. This study uses cutting-edge AI to analyze individual muscles across the whole body from MRI scans, creating the most detailed dataset of human muscle and bone relationships to date. The findings reveal how skeletal size ("frame size") and body dimensions shape muscularity, providing new insights into why people differ in muscle size and strength. These results have broad implications, from optimizing athletic training to diagnosing and treating muscle disorders. By uncovering the intricate connections between muscles, bones, and body size, this research offers a powerful framework for understanding human movement and variability.

bioengineering↗

bioRxiv2025

Integrated spatial morpho-transcriptomics predicts functional traits in pancreatic cancer

Analyses of patient-derived cell lines have greatly enhanced discovery of molecular biomarkers and therapeutic targets. However, characterization of cellular morphological properties is limited. We studied cell morphologies of human pancreatic adenocarcinoma (PDAC) cell lines and their associations with drug sensitivity, gene expression, and functional properties. By integrating live cell and spatial mRNA imaging, we identified KRAS inhibitor-induced morphological changes specific for drug-resistant cells that correlated with gene expression changes. We then categorized a large panel of patient-derived PDAC cell lines into morphological (e.g., polygonal, irregular, spheroid) and organizational (e.g., tightly aggregated, multilayered, dispersed) subtypes and found differences in gene expression, therapeutic targeting potential, and metastatic proclivity. In human PDAC tissues, we identified prognostic expression signatures associated with distinct cancer cell organization patterns. In summary, we highlight the potential of cell morphological information in rapid, cost-effective assays to aid precision oncology efforts leveraging patient-derived in vitro models and tissues.

cancer biology↗

bioRxiv2024

Sub-cellular Imaging of the Entire Protein-Coding Human Transcriptome (18933-plex) on FFPE Tissue Using Spatial Molecular Imaging

Single-cell RNA-seq revolutionized single-cell biology, by providing a complete whole transcriptome view of individual cells. Regrettably, this was accomplished only for individual, tissue-dissociated cells. High-plex spatial biology has begun to recover the x, y, and z-coordinates of single-cells, but typically at the expense of far less than whole transcriptome coverage. To solve this problem, Bruker Spatial Biology has accomplished a commercial-grade panel (CosMx(R) Spatial Molecular Imager Whole Transcriptome Panel (WTx)), using 37,872 imaging barcodes, capable of sub-cellular imaging of the entire human protein-coding transcriptome. The imaging barcodes are encoded with 156 bits of information (4 on-cycles and 35 dark-cycles per code), at a Hamming Distance of 4 from each other to achieve a very low false-code detection. Key to achieving this high-plex capability was the ability to manufacture imaging barcodes that require no in-tissue amplification (every barcode is manufactured under GMP to contain exactly 30 fluorescent dyes) and uniform, size-exclusion purified, extremely small imaging barcodes ([~] 20 nm). A detailed study of six different human FFPE tissue types was performed (Colon, Pancreas, Hippocampus, Skin, Breast, Kidney), yielding over 5.4 billion transcripts from 2.7 million cells. We counted over 1,550 transcripts-per-cell on average and observed 900 unique genes per cell (measured as the median). Single fixed-cells containing well over 10,000 subcellularly imaged transcripts were accomplished. Advancing single-cell imaging to the whole transcriptome level opens a single unified approach to accomplish essentially all single-cell experiments, both imaging and non-imaging. Depending upon the sample type (e.g. fixed-cells, organoids, tissue sections, etc.), the transcripts per cell and genes per cell measured using the whole transcriptome panel often exceeds that obtained by the highest-resolution single-cell RNA-seq, can be performed on a single 5 {micro}m FFPE tissue section, with no dissociation bias (every cell is counted). Pathway analysis within the tumor bed of a colon adenocarcinoma sample found evidence of enrichment in pathways suggestive of an aggressive tumor type, and localized ligand-receptor analysis showed spatially restricted patterns related to adhesion, migration, and proliferation. The high-dimensional whole transcriptome data is streamed directly to a cloud-based Spatial Informatics Platform, allowing for the scalable processing of millions-of-single-cells and billions-of-transcripts per operation. The WTx data are combined with high-resolution antibody-based cell-morphology imaging and data-driven machine-learning cell segmentation algorithms, to generate the most complete view of single cell and sub-cellular spatial biology that has ever been obtained.

genomics↗

bioRxiv2024

E protein control of NKgammadeltaT cell development through both generation and function of the stereotypic Vgamma1Vdelta6.3 TCR

T cell receptor (TCR) signals regulate important developmental transitions through induction of the E protein antagonist, Id3; however, Id3-deficiency produces paradoxical effects on {gamma}{delta} T cell subsets. Here, we show here that Id3-deficiency attenuates the development of V{gamma}3-expressing {gamma}{delta} T cells, while markedly enhancing the development of V{gamma}1V{delta}6.3-expressing NK{gamma}{delta}T cells. Id3-deficiency does so by regulating both the generation of the stereotypic V{gamma}1V{delta}6.3 TCR expressed by NK{gamma}{delta}T cells and its capacity to support development. Indeed, we determined that the Trav15 segment, which encodes the V{delta}6.3 TCR subunit, is directly bound by E proteins that control its expression. Moreover, once expressed, the resulting V{gamma}1V{delta}6.3 TCR in capable of specifying the innate-like NK{gamma}{delta}T cell fate in a cell-autonomous and developmentally-unrestricted manner that is restrained by the Id3/E axis. Together, these data indicate that the paradoxical behavior of NK{gamma}{delta}T cells in the Id3-deficient setting is entirely determined by its stereotypic V{gamma}1V{delta}6.3 TCR complex.

immunology↗

bioRxiv2023

Ribosomal protein control of hematopoietic stem cell transformation through direct, non-canonical regulation of metabolism

We report here that expression of the ribosomal protein, RPL22, is frequently reduced in human myelodysplastic syndrome (MDS) and acute myelogenous leukemia (AML); and, reduced RPL22 expression is associated with worse outcomes. Mice null for Rpl22 display characteristics of an MDS-like syndrome and develop leukemia at an accelerated rate. Rpl22-deficient mice also display enhanced hematopoietic stem cell (HSC) self-renewal and obstructed differentiation potential, which arises not from reduced protein synthesis but from altered metabolism including increased fatty acid oxidation (FAO) and a striking induction of the stemness factor Lin28b in the resulting leukemia. Lin28b promotes a substantial increase in lipid content, upon which the survival of Rpl22-deficient leukemias depends. Altogether, these findings reveal that Rpl22 insufficiency enhances the leukemia potential of HSC through regulation of FAO and promotes leukemogenesis through Lin28b promotion of lipid synthesis. HighlightsO_LIRPL22 insufficiency is observed in MDS/AML and is associated with reduced survival C_LIO_LIRpl22-deficiency produces an MDS-like syndrome and facilitates leukemogenesis C_LIO_LIRpl22-deficiency does not impair global protein synthesis by HSC C_LIO_LIRpl22 controls leukemia survival through control of lipid synthesis C_LI eTOC: Rpl22 controls the function and transformation potential of hematopoietic stem cells through regulation of lipid metabolism.

cancer biology↗

bioRxiv2022

Back to the Meadow Brown: eyespot variation and field temperature in a classic butterfly polymorphism

Since the classic work of E.B. Ford, alternate hypotheses have focused on explaining eyespot variation in the Meadow Brown butterfly strictly as a genetic polymorphism and the role of temperature in this classic example of natural selection has therefore been overlooked. Here we use large and continuous field collections from three sites in the UK to examine the effect of field temperature on total eyespot variation using the same presence/absence scoring as Ford. We show that higher developmental temperatures in the field lead to the disappearance of the spots visible while the butterfly is at rest, explaining Fords original observation that hindwing spotting declines across the season as temperatures increase. Analysis of wing damage supports the historical hypothesis that hindwing spots confuse aerial predators. However, as hindwing spotting declines over the season, a trade-off is suggested between their role in deflecting predators early in the season and their later developmental cost. In contrast, the large forewing eyespot is always present, scales with forewing length and its variation is best explained by day of the year rather than developmental temperature. As this large forewing spot is thought to be involved in startling predators, its constant presence is therefore likely required for defence. We model annual total spot variation with phenological data from the UK and derive predictions as to how spot patterns will continue to change under increasing summer temperatures, predicting that spotting will continue to decrease both across a single season and year or year as our climate warms. Summary statementWe show that a long-held example of genetic polymorphism, eyespot variation is the Meadow Brown butterfly, is correlated with field temperature during butterfly development.

ecology↗

bioRxiv2022

STORM-seq Reveals Differentiation Trajectories of Primary Human Fallopian Tube Epithelium

Despite significant advances, current single-cell RNA sequencing (scRNA-seq) technologies often struggle with accurately detecting non-coding transcripts, achieving full-length RNA coverage, and/or resolving transcript-level complexity. Many are also difficult to implement or inaccessible without specialized liquid handlers, further limiting their utility. We present Single-cell TOtal RNA-seq Miniaturized (STORM-seq), a random- hexamer primed, ribo-reduced single-cell total RNA sequencing (sc-total-RNA-seq) protocol using standard laboratory equipment. Adapted as a kit, STORM-seq constructs sequence-ready libraries in one working day, producing the highest complexity scRNA- seq libraries to-date, robustly measuring transcript isoforms and clinically relevant gene fusions in single cells. STORM-seq faithfully reconstructs expression profiles of locus- level transposable elements (TEs), and provides high-resolution profiling of transient, low- abundance enhancer RNAs (eRNAs), offering a powerful tool to dissect single-cell gene regulatory networks in unprecedented detail. Applied to human fallopian tube epithelium, the improved transcriptional resolution reveals a putative progenitor-like population and intermediate cell states, shaped by TEs and non-coding RNAs.

molecular biology↗

bioRxiv2021

High-Plex Multiomic Analysis in FFPE Tissue at Single-Cellular and Subcellular Resolution by Spatial Molecular Imaging

The Spatial Molecular Imaging platform (CosMxTM SMI, NanoString Technologies, Seattle, WA) utilizes high-plex in-situ imaging chemistry for both RNA and protein detection. This automated instrument provides 1000s of plex, at high sensitivity (1 to 2 copies/cell), very low error rate (0.0092 false calls/cell) and background ([~]0.04 counts/cell). The imaging system generates three-dimensional super-resolution localization of analytes at [~]2 million cells per sample, four samples per run. Cell segmentation is morphology-based using antibodies, compatible with FFPE samples. Multiomic data (980 RNAs, 108 proteins) were measured at subcellular resolution using FFPE tissues (non-small cell lung (NSCLC) and breast cancer) and allowed identification of over 18 distinct cell types, 10 unique tumor microenvironments, and 100 pairwise ligand-receptor interactions. Over 800,000 single cells and [~]260 million transcripts data are released into the public domain allowing extended data analysis by the entire spatial biology research community.

genomics↗

bioRxiv2021

Spatially resolved whole transcriptome profiling in human and mouse tissue using Digital Spatial Profiling

Emerging spatial profiling technology has enabled high-plex molecular profiling in biological tissues, preserving the spatial and morphological context of gene expression. Here we describe expanding the chemistry for the Digital Spatial Profiling platform to quantify whole transcriptomes in human and mouse tissues using a wide range of spatial profiling strategies and sample types. We designed multiplexed in situ hybridization probe pools targeting the protein-coding genes in the human and mouse transcriptomes, hereafter referred to as the human or mouse Whole Transcriptome Atlas (WTA). We validated the human and mouse WTA using cell lines to demonstrate concordance with orthogonal gene expression profiling methods in profiled region sizes ranging from ~10-500 cells. By benchmarking against bulk RNAseq and fluorescence in situ hybridization, we demonstrate robust transcript detection possible down to ~100 transcripts per region. To assess the performance of WTA across tissue and sample types, we applied WTA to biological questions in cancer, molecular pathology, and developmental biology. We show that spatial profiling with WTA can detect expected spatial gene expression differences between tumor and tumor microenvironment, identify spatial disease-specific heterogeneity in gene expression in histological structures of the human kidney, and comprehensively map transcriptional programs in anatomical substructures of nine organs in the developing mouse embryo. Digital Spatial Profiling technology with the WTA assays provides a flexible method for spatial whole transcriptome profiling applicable to diverse tissue types and biological contexts.

genomics↗

bioRxiv2021

Transcriptome-wide spatial RNA profiling maps the cellular architecture of the developing human neocortex

Spatial genomic technologies can map gene expression in tissues, but provide limited potential for transcriptome-wide discovery approaches and application to fixed tissue samples. Here, we introduce the GeoMX Whole Transcriptome Atlas (WTA), a new technology for transcriptome-wide spatial profiling of tissues with cellular resolution. WTA significantly expands the Digital Spatial Profiling approach to enable in situ hybridisation against 18,190 genes at high-throughput using a sequencing readout. We applied WTA to generate the first spatial transcriptomic map of the fetal human neocortex, validating transcriptome-wide spatial profiling on formalin-fixed tissue material and demonstrating the spatial enrichment of autism gene expression in deep cortical layers. To demonstrate the value of WTA for cell atlasing, we integrated single-cell RNA-sequencing (scRNA-seq) and WTA data to spatially map dozens of neural cell types and showed that WTA can be used to directly measure cell type specific transcriptomes in situ. Moreover, we developed computational tools for background correction of WTA data and accurate integration with scRNA-seq. Our results present WTA as a versatile transcriptome-wide discovery tool for cell atlasing and fixed tissue spatial transcriptomics.

genomics↗