Biology subjects

Campbell, K. M.

Publications and source records attributed to Campbell, K. M..

10 recordsLinked to original sources

bioRxiv2026

CytoRipple: Mapping Tissue Polarization through Subcellular Cytoskeleton Dynamics

Spatial multi-omic technologies entail simultaneous measurement of tens-to-thousands of molecular features (e.g. RNA, protein). While cell type and state profiling is the major focus of spatial multi-omic analyses, cytoskeletal proteins are often solely utilized for cell segmentation and quality control. Here, we introduce a new computational framework, CytoRipple, that leverages these concomitant spatially-resolved single-cell measurements of cytoskeleton proteins to identify tissue-level vector fields. Polarization vectors are known to associate with cell movement and phenotype, inferring their physical orientation. We validate these associations across biological contexts and reveal how polarization vector fields can be harnessed to discover multi-cellular structures (e.g. "streams" of cells with similar polarizations). These multi-cellular cytoskeleton dynamics associate with distinct phenomena, such as vector "divergence" with CD8+ T cell activation and "streams" with spatially segmented stages of the anti-tumor immune response. Together, these data support a role for CytoRipple in revealing a dynamic layer across spatial multi-omic platforms.

bioinformatics↗

bioRxiv2026

SpaceBender: Denoising Spatial Transcriptomics Data to Enhance Biological Signals

Spatial transcriptomics (ST) allows for the simultaneous profiling of cell phenotype (e.g. transcriptome) and physical position. Although ST data has brought about numerous new biological insights, it remains limited by noise, largely in the form of RNA diffusion. Here, we introduce SpaceBender which leverages spatial-specific information (e.g. spatial ambient RNA niches) to build upon single-cell denoising strategies. SpaceBender outperforms current ST denoising methods in simulations and in vivo chimeric tissues. Through case studies, we demonstrate how SpaceBender unveils hidden biological insights and increases the significance of said insights as evaluated by statistical testing. Finally, we reveal how SpaceBender may also be applied to subcellular resolution data where it removes off-target expression of neighboring cell type specific marker genes. In all, we present SpaceBender as an ST denoising method, freely available as an open-source package, that may enhance the insights the field may draw from various ST data types.

bioinformatics↗

bioRxiv2024

Detecting Clinically Relevant Topological Structures in Multiplexed Spatial Proteomics Imaging Using TopKAT

Novel multiplexed spatial proteomics imaging platforms expose the spatial architecture of cells in the tumor microenvironment (TME). The diverse cell population in the TME, including its spatial context, has been shown to have important clinical implications, correlating with disease prognosis and treatment response. The accelerating implementation of spatial proteomic technologies motivates new statistical models to test if cell-level images associate with patient-level endpoints. Few existing methods can robustly characterize the geometry of the spatial arrangement of cells and also yield both a valid and powerful test for association with patient-level outcomes. We propose a topology-based approach that combines persistent homology with kernel testing to determine if topological structures created by cells predict continuous, binary, or survival clinical endpoints. We term our method TopKAT (Topological Kernel Association Test) and show that it can be more powerful than statistical tests grounded in the spatial point process model, particularly when cells arise along the boundary of a ring. We demonstrate the properties of TopKAT through simulation studies and apply it to two studies of triple negative breast cancer where we show that TopKAT recovers clinically relevant topological structures in the spatial distribution of immune and tumor cells.

bioinformatics↗

bioRxiv2024

A Spatial Omnibus Test (SPOT) for Spatial Proteomic Data

Spatial proteomics can reveal the spatial organization of immune cells in the tumor immune microenvironment. Relating measures of spatial clustering, such as Ripleys K or Besags L, to patient outcomes may offer important clinical insights. However, these measures require pre-specifying a radius in which to quantify clustering, yet no consensus exists on the optimal radius which may be context-specific. We propose a SPatial Omnibus Test (SPOT) which conducts this analysis across a range of candidate radii. At each radius, SPOT evaluates the association between the spatial summary and outcome, adjusting for confounders. SPOT then aggregates results across radii using the Cauchy combination test, yielding an omnibus p-value characterizing the overall degree of association. Using simulations, we verify that the type I error rate is controlled and show SPOT can be more powerful than alternatives. We also apply SPOT to an ovarian cancer study. An R package and tutorial is provided at https://github.com/sarahsamorodnitsky/SPOT.

bioinformatics↗

bioRxiv2020

Optimizing nucleic acid extraction and transcriptome evaluation from low-input, fixed clinical samples

Tumor biopsies are commonly formalin-fixed and paraffin-embedded (FFPE) for long-term and efficient storage. However, FFPE preservation can greatly compromise the quality of samples, the extraction of nucleic acids, and feasibility of downstream studies, including RNA sequencing. These challenges are especially evident in the studies of clinical trial samples, where the sizes of biopsies often limit the amount of material available for study. Here, we evaluate two nucleic acid extraction kits (Covaris truXTRAC FFPE tNA Plus, QIAGEN miRNeasy FFPE) and three hybridized-capture-based RNA sequencing library preparations (Agilent SureSelect XT RNA Direct, Agilent SureSelect XT HS, and Illumina TruSeq RNA Exome) to evaluate the impact of these sample processing steps on transcriptome evaluation in a melanoma biopsy procured in a clinical setting and preserved by FFPE. While there exist many options for extraction and library preparation that are appropriate for RNA sequencing from FFPE samples, we observed that combinations of experimental approaches may have subtle impacts on downstream analysis, including gene expression quantification and fusion detection.

genomics↗

bioRxiv2020

Prediction of SARS-CoV-2 epitopes across 9360 HLA class I alleles

SummarySARS-CoV-2 T cell response assessment and vaccine development may benefit from an approach that considers the global landscape of the human leukocyte antigen (HLA) proteins. We predicted the binding affinity between 9-mer and 15-mer peptides from the SARS-CoV-2 peptidome for 9,360 class I and 8,445 class II HLA alleles, respectively. We identified 368,145 unique combinations of peptide-HLA complexes (pMHCs) with a predicted binding affinity less than 500nM, and observed significant overlap between class I and II predicted pMHCs. Using simulated populations derived from worldwide HLA frequency data, we identified sets of epitopes predicted in at least 90% of the population in 57 countries. We also developed a method to prioritize pMHCs for specific populations. Collectively, this public dataset and accessible user interface (Shiny app: https://rstudio-connect.parkerici.org/content/13/) can be used to explore the SARS-CoV-2 epitope landscape in the context of diverse HLA types across global populations.Competing Interest StatementK.M.C is a shareholder in Geneoscopy LLC. D.K.W. is a founder, equity holder and receives consulting fees from Immunai. A.R. is supported by the National Institute of Health (R35 CA197633), the Ressler Family Fund, the Agilent Thought Leader Award, a Stand Up to Cancer- Bristol-Meyer Squibb Catalyst Research Grant (Grant Number: SU2C-AACR-CT06-17). This research grant is administered by the American Association for Cancer Research, the scientific partner of SU2C. A.R. is a member researcher at the Parker Institute for Cancer Immunotherapy.View Full Text

immunology↗

bioRxiv2019

Stool-derived eukaryotic RNA biomarkers for detection of high-risk adenomas

Background and aimsColorectal cancer (CRC) is the second leading cause of cancer related deaths in the United States. Mortality is largely attributable to low patient compliance with screening and a subsequent high frequency of late-stage diagnoses. Noninvasive methods, such as stool- or blood-based diagnostics could improve patient compliance, however, existing techniques cannot adequately detect high-risk adenomas (HRAs) and early-stage CRC. MethodsHere we apply cancer profiling using amplicon sequencing of stool-derived eukaryotic RNA for 275 patients undergoing prospective CRC screening. A training set of 154 samples was used to build a random forest model that included 4 feature types (differentially expressed amplicons, total RNA expression, demographic information, and fecal immunochemical test results). An independent hold out test set of 121 patients was used to assess model performance. ResultsWhen applied to the 121-patient hold out test set, the model attained a receiver operating characteristic (ROC) area under the curve (AUC) of 0.94 for CRC and a ROC AUC of 0.87 for CRC and HRAs. In aggregate, the model achieved a 91 % sensitivity for CRC and a 73% sensitivity for HRAs at an 89% specificity for all other findings (medium-risk adenomas, low-risk adenomas, benign polyps, and no findings on a colonoscopy). ConclusionCollectively, these results indicate that in addition to early CRC detection, stool-derived biomarkers can accurately and noninvasively identify HRAs, which could be harnessed to prevent CRC development for asymptomatic, average-risk patients.

cancer biology↗

bioRxiv2018

Development of a precision oncology single molecule molecular inversion probe (smMIP) panel using a community-consensus approach

Clinical targeted sequencing panels are important for identifying actionable variants for cancer patients, however, there are currently no strategies to create impartial and rationally-designed panels to accommodate rapidly growing knowledge within the field. Here we use the Clinical Interpretations of Variants in Cancer database (CIViC) in conjunction with single-molecule molecular inversion probe (smMIP) capture to identify and design probes targeting clinically relevant variants in cancer. In total, 2,027 smMIPs were designed to target 111 eligible CIViC variants. The total genomic region covered by the CIViC smMIPs reagent was 61.5 kb. When compared to existing genome or exome sequencing results (n = 27 cancer samples from 5 tumor types), CIViC smMIP sequencing demonstrated a 95% sensitivity for variant detection (n = 61/64 variants). Variant allele frequency for variants identified on both sequencing platforms were highly concordant (Pearson correlation = 0.885; n = 61 variants). Moreover, for individuals with paired tumor/normal samples (n = 12), 182 clinically relevant variants missed by original sequencing were discovered by CIViC smMIPs sequencing. This design paradigm demonstrates the utility of an open-sourced database built on attendant community contributions for each variant with peer-reviewed interpretations. Use of a public repository for variant identification, probe development, and variant annotation could provide a transparent approach to build a dynamic next-generation sequencing-based oncology panel.

bioinformatics↗

bioRxiv2018

Spontaneous aggressive ERα+ mammary tumor model is driven by Kras activation

The NRL-PRL murine model, defined by mammary-selective transgenic rat prolactin ligand rPrl expression, establishes spontaneous ER+ mammary tumors, mimicking the association between elevated prolactin (PRL) and risk for development of ER+ breast cancer in postmenopausal women. Whole genome and exome sequencing in a discovery cohort (n=5) of end stage tumors revealed canonical activating mutations and copy number amplifications of Kras. The frequent mutations in this pathway were validated in an extension cohort, identifying activating Ras alterations in 79% (23/29) of tumors. Transcriptome analyses over the course of oncogenesis revealed marked alterations associated with Ras activity in established tumors, compared to preneoplastic tissues, in cell-intrinsic processes associated with mitosis, cell adhesion and invasion, as well as in the tumor microenvironment, including immune activity. These genomic analyses suggest that PRL induces a selective bottleneck for spontaneous Ras-driven tumors which may model a subset of aggressive clinical ER+ breast cancers.

cancer biology↗

bioRxiv2018

Standard operating procedure for somatic variant refinement of tumor sequencing data

PurposeManual review of aligned sequencing reads is required to develop a high-quality list of somatic variants from massively parallel sequencing data (MPS). Despite widespread use in analyzing MPS data, there has been little attempt to describe methods for manual review, resulting in high inter- and intra-lab variability in somatic variant detection and characterization of tumors.\n\nMethodsOpen source software was used to develop an optimal method for manual review setup. We also developed a systemic approach to visually inspect each variant during manual review.\n\nResultsWe present a standard operating procedures for somatic variant refinement for use by manual reviewers. The approach is enhanced through representative examples of 4 different manual review categories that indicate a reviewers confidence in the somatic variant call and 19 annotation tags that contextualize commonly observed sequencing patterns during manual review. Representative examples provide detailed instructions on how to classify variants during manual review to rectify lack of confidence in automated somatic variant detection.\n\nConclusionStandardization of somatic variant refinement through systematization of manual review will improve the consistency and reproducibility of identifying true somatic variants after automated variant calling.

genetics↗