https://doi.org/10.5061/dryad.8gtht76vw

This repository contains 15 .FCS (Flow Cytometry Standard) data files. Each of these files represents a sample collected from a BCP-ALL patient or a healthy control patient.

Each BCP-ALL patient has samples collected from 3 timepoints and 2 tissues: diagnosis (bone marrow and blood), day 8 post-treatment initiation with chemotherapy (blood), and day 15 post-treatment initiation (blood). Healthy patients only have samples collected from a single timepoint (the time of donation) and one tissue (bone marrow).

Description of the data and file structure

The information about the patient, timepoint, and tissue information about each sample is encoded in the .FCS filename. The .FCS filename is a string formatted as follows:

{patient_name}{tissue}{timepoint}.fcs.

In this string, {patient_name} is one of the following:

• "patient_1" (a BCP-ALL patient)
• "patient_2" (a BCP-ALL patient)
• "patient_3" (a BCP-ALL patient)
• "healthy_1" (a healthy donor)
• "healthy_2" (a healthy donor)
• "healthy_3" (a healthy donor)

{tissue} is one of the following:

• "bm" (bone marrow)
• "pb" (peripheral blood)

and {timepoint} is one of the following:

• "diagnosis" (the diagnostic timepoint for a BCP-ALL patient)
• "+8" (The timepoint corresponding to 8 days post-initiation of chemotherapy treatment)
• "+15" (The timepoint corresponding to 15 days post-initiation of chemotherapy treatment)
• "healthy" (The timepoint at which the healthy donor donated their cells; i.e. the only timepoint available for healthy controls.)

All cells have been analyzed for the presence of 28 proteins as previously described using mass cytometry (CyTOF), a high-dimensional cytometry platform similar to multicolor flow cytometers commonly used to analyze leukemic tissue specimens in clinical laboratories. CyTOF analysis allows a high-dimensional sample characterization, extending the capabilities beyond those of conventional multicolor flow cytometers, typically employed for the analysis of leukemic tissue specimens in clinical laboratories.

Each file includes information about 28 proteins as read off the mass cytometer (unit: ion counts) and an additional column called cell_type that encodes healthy cells with a value of 0 and cancerous cells as a value of 1. These labels were manually annotated by an expert BCP-ALL cytometrist and verified by a physician-scientist board-certified in pediatric hematology and oncology. The samples were extracted, debarcoded, and filtered for doublets and dead cells according to standard mass cytometry protocols. The ion counts for each protein have NOT been transformed in any way.

Software and Workflow

The data files are in Flow Cytometry Standard (.fcs) format and can be analyzed using free, open-source software.

R

We recommend using the following R packages:

• flowCore: for reading and preprocessing .fcs files
• tidyFlowCore: for tidyverse-style workflows with flow cytometry data
• tidytof: for downstream analysis, including clustering and visualization

Python

For Python users, we recommend pytometry for reading, transforming, and analyzing .fcs files using the Scanpy ecosystem

These packages allow users to load, subset, and analyze single-cell protein expression data from mass cytometry experiments. No analysis scripts are included in this dataset submission.

Sharing/Access information

This is a proprietary dataset that has not been published anywhere else. If you access it for use in an academic paper, we encourage citing this Dryad repository.

Human subjects data

All human samples and associated data in this study were collected under protocols approved by the Stanford University Institutional Review Board (IRB). Informed consent was obtained from all participants or their legal guardians, including explicit consent to publish de-identified data in public repositories.

The data shared in this submission have been fully de-identified in accordance with applicable legal and ethical standards. No personally identifiable information, including names, dates of birth, medical record numbers, or geographic identifiers, is included. Sample identifiers (e.g., ID numbers) are randomly assigned codes that cannot be traced back to individual participants.

Only single-cell protein expression data derived from mass cytometry are included. These data do not contain genetic information or any direct or indirect identifiers.