DOI: 10.5061/dryad.573n5tbn8
title: Integrating single-cell biophysical and transcriptomic features to resolve functional heterogeneity in mantle cell lymphoma

description: Intra-tumor heterogeneity impacts disease progression and therapeutic
 resistance but remains poorly characterized by conventional histologic,
 immunophenotypic, and molecular approaches. Single-cell biophysical
 properties distinguish functional phenotypes complementary to these
 approaches, providing additional insight into cellular diversity. Here, we
 link both buoyant mass and stiffness to gene expression to identify
 clinically relevant phenotypes within primary mantle cell lymphoma (MCL)
 cells, employing MCL as a model of biological and clinical diversity in
 human cancer. Linked measurements reveal that buoyant mass and stiffness
 characterize B-cell development states from naïve to plasma cell and
 correlate with expression of oncogenic B-cell receptor signaling genes
 such as BLK and CD79A. Additionally, changes in cell buoyant mass within
 primary patient specimens ex vivo correlate with sensitivity to
 Bruton's Tyrosine Kinase inhibitors in vivo in MCL and chronic
 lymphocytic leukemia, another B-cell malignancy. These findings highlight
 the value of biophysical properties as biomarkers of response in pursuit
 of future precision therapeutic strategies.
description: # Integrating single-cell biophysical and transcriptomic features to
 resolve functional heterogeneity in mantle cell lymphoma Dataset DOI:
 [10.5061/dryad.573n5tbn8](https://doi.org/10.5061/dryad.573n5tbn8) ##
 Description of the data and file structure This dataset includes
 single-cell biophysical measurements, single-cell RNA-seq, and bulk
 RNA-seq presented in Zhang and Debaize (in review), profiling 3 PDX models
 of mantle cell lymphoma. The relevant manifests outline the sample
 provenance, including the tissue from which the PDX cells were isolated
 from murine hosts. For the single-cell dataset, biophysical properties
 were measured prior to sorting into well plates, so linked biophysical
 profiles and transcriptomic libraries are available for each cell.
 Included are the raw transcriptomic count matrices after alignment, as
 well as the R data objects after QC filtering, normalization, and
 preprocessing, which were used for downstream analysis and visualization.
 ### Files and variables #### File:
 singleCell_dataset.cell_biophysical_manifest_.v1.0.txt
 **Description:** Per-cell annotations for the single-cell dataset,
 including biophysical measurements. ##### Variables * name: Unique cell ID
 * batch: Batch of sequencing submitted * tis: Mouse tissue from which the
 sample was harvested * well: Well of the plate submitted for sequencing *
 seqid: Sequencing ID for sample * date: Date of preparation * model: PDX
 model ID * mouseno: Unique mouse ID * isdoublet: Doublet estimation *
 mass: Buoyant mass (pg) * ND: Node deviation * ID: Other sample ID * Vol:
 Volume (fL) * ND/V: Node deviation / volume * ND/V2: Node deviation /
 volume^2^ * ND/BM: Node deviation / buoyant mass * SNACS: Size-Normalized
 Acoustic Scattering * SNACS2: Alternative calculation for SNACS (not used
 in manuscript) #### File: bulk_dataset.sample_manifest.v1.0.txt
 **Description:** Annotations for the bulk RNA-seq dataset of PDX models.
 ##### Variables * name: Name of sample * model: PDX model ID * group:
 Sequencing batch * tis: Murine tissue of origin #### File:
 singleCell_dataset.sample_manifest.v1.0.txt **Description:** Per-sample
 annotations for the single-cell dataset, outlining mouse ID, plate number,
 and tissue of origin. ##### Variables * batch: Batch of sequencing *
 seqid: Unique sequencing ID * plate: Plate ID * model: PDX model ID *
 mouseno: Mouse number * mouseid: Mouse ID * date: Date of sequencing *
 tis: Murine tissue of origin #### File:
 singleCell_dataset.merged_cells.seurat_processed.v2.4.rds
 **Description:** R data object of Seurat-processed, pre-filtered, and
 normalized single-cell sequencing data, from which downstream analyses and
 visualizations were produced. #### File:
 singleCell_dataset.raw_count_matrix.v1.0.txt **Description:** Raw
 transcript-level count matrix for the single-cell RNA-seq dataset after
 alignment and abundance quantification. #### File:
 bulk_dataset.raw_count_matrix.v1.0.txt **Description:** Raw
 transcript-level count matrix for the bulk RNA-seq dataset after alignment
 and abundance quantification. #### File:
 bulk_dataset.DESeq2_processed.v1.0.rds **Description:** R data object of
 DESeq2-processed, pre-filtered, and normalized bulk sequencing data, from
 which downstream analyses and visualizations were produced.