Integrating single-cell biophysical and transcriptomic features to resolve functional heterogeneity in mantle cell lymphoma
Data files
Oct 31, 2025 version files 284.58 MB
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bulk_dataset.DESeq2_processed.v1.0.rds
24.19 MB
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bulk_dataset.raw_count_matrix.v1.0.txt
2.59 MB
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bulk_dataset.sample_manifest.v1.0.txt
619 B
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README.md
3.30 KB
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singleCell_dataset.cell_biophysical_manifest_.v1.0.txt
539.95 KB
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singleCell_dataset.merged_cells.seurat_processed.v2.4.rds
27.17 MB
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singleCell_dataset.raw_count_matrix.v1.0.txt
230.07 MB
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singleCell_dataset.sample_manifest.v1.0.txt
9.94 KB
Abstract
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.
Dataset DOI: 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 / volume2
- 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.
