SDRF-Proteomics

1. Status of this Template

This document provides guidelines for annotating single cell proteomics (SCP) experiments in SDRF-Proteomics format. This template extends the core SDRF-Proteomics specification with single-cell-specific metadata fields.

Status: Released - This template is aligned with community guidelines and stable for production use.

Version: 1.0.0 - 2026-01

2. Alignment with Community Guidelines

This template is aligned with the Nature Methods single-cell proteomics guidelines:

Gatto L, Aebersold R, Cox J, et al. (2023). Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments. Nature Methods, 20(3), 375-386. https://doi.org/10.1038/s41592-023-01785-3

The metadata columns and requirements in this template follow the recommendations from this community consensus paper for standardizing SCP experiment reporting.

3. Abstract

Single cell proteomics (SCP) characterizes the proteome at the level of individual cells, providing insights into cellular heterogeneity, rare cell populations, cellular state transitions, and protein expression dynamics at single-cell resolution.

SCP experiments have unique characteristics: cell isolation methods (FACS, microfluidics, cellenONE), ultra-low sample input (picogram to nanogram), carrier proteome strategies, cell-level metadata requirements, and high-throughput multiplexing (SCoPE-MS, TMT). This template defines the additional metadata requirements for annotating single cell proteomics datasets.

4. Connections to Other Omics Fields

Single cell proteomics data is often integrated with:

  • Single-cell RNA sequencing (scRNA-seq): For multi-omic cellular characterization

  • Single-cell ATAC-seq: For chromatin accessibility correlation

  • Spatial transcriptomics: When cells have spatial context

  • Flow cytometry data: For pre-sorting characterization (FSC, SSC, sorting markers)

When available, linking to related single-cell omics datasets through BioSamples accession numbers or shared cell identifiers is highly RECOMMENDED.

5. Additional Ontologies

In addition to the ontologies supported by the core SDRF-Proteomics specification, single cell proteomics templates utilize:

  • Cell Ontology (CL): For cell type classification (https://obofoundry.org/ontology/cl.html)

  • Human Cell Atlas ontologies: For standardized cell annotations

  • EDAM ontology: For bioinformatics data types and operations

  • Human/Mouse Developmental Stage ontologies (HsapDv, MmusDv): For developmental stage annotation

6. Checklist

This section defines the metadata columns required and recommended for single cell proteomics experiments, following the Nature Methods SCP guidelines.

6.1. Required Columns

The following columns are REQUIRED for single cell proteomics experiments in addition to the core SDRF-Proteomics requirements:

Column Name Description Cardinality Controlled Values Example Values

comment[sample type]

Classification of the sample - distinguishes single cells from carriers, references, and controls. Critical for proper data analysis.

1

single cell, carrier, reference, empty, negative control, bulk control, not applicable

single cell, carrier, reference, empty

characteristics[single cell isolation method]

Technique used to isolate individual cells

1

FACS, cellenONE, CellenONE, microfluidics, laser capture microdissection, LCM, manual picking, nanoPOTS, droplet microfluidics, acoustic droplet ejection, not applicable

FACS, cellenONE, laser capture microdissection

characteristics[cell identifier]

Unique identifier for each single cell within the experiment. Required per SCP guidelines for tracking cells through analysis.

1

Alphanumeric identifier (use carrier, reference, empty for non-single-cell samples)

cell_001, SC_A1, well_B3, barcode_ATCGATCG, carrier, reference

6.2. Recommended Columns

The following columns are RECOMMENDED for single cell proteomics experiments per the Nature Methods guidelines:

Column Name Description Cardinality Ontology/CV Example Values

characteristics[individual]

Unique identifier for the donor/patient individual. Important for studies with multiple donors.

0..1

Alphanumeric identifier

patient_001, donor_A, C1, T1

comment[sample preparation batch]

Batch identifier for sample preparation (plate, chip, processing batch). Critical for batch effect correction.

0..1

Free text identifier

plate1, plate2, chip_A, batch_20220601

comment[cells per well]

Number of cells per well/reaction. Use 1 for true single cells, higher numbers for small pools.

0..1

Numeric value

1, 5, 10, 100

characteristics[developmental stage]

Developmental stage of the organism at sample collection

0..1

EFO, HsapDv, MmusDv, UBERON

adult, embryonic stage, fetal stage, neonate

6.3. Optional Columns - Flow Cytometry Data

Per the Nature Methods guidelines, flow cytometry data should be reported when available:

Column Name Description Example Values

characteristics[forward scatter]

Forward scatter (FSC) value from flow cytometry - proxy for cell size

316.0, 250, not applicable

characteristics[side scatter]

Side scatter (SSC) value from flow cytometry - proxy for cell granularity/complexity

301.0, 184, not applicable

characteristics[sorting marker]

Markers used for cell sorting/selection with optional intensity values

CD45+, GFP+, CD3+CD4+, CD34:APC-Cy7-A=276.0, PI-

6.4. Optional Columns - Cell State and Quality

Column Name Description Example Values

characteristics[cell viability]

Viability status of the cell at isolation

live, viable, dead, unknown

characteristics[cell cycle phase]

Cell cycle phase if determined (e.g., by FACS or computational inference)

G1, S, G2, G2/M, M, G0, not determined

characteristics[cell diameter]

Physical diameter of the isolated cell if measured (in micrometers)

15 um, 20.5 um, 12 μm

comment[single cell quality]

Quality assessment of the single cell preparation (pass/fail based on QC criteria)

pass, fail, OK, not OK

6.5. Optional Columns - Model Organisms

Column Name Description Example Values

characteristics[genotype]

Genotype of the organism (especially for model organisms)

wild type genotype, Pitx2-/-, GFP+, daf-2(e1370)

characteristics[strain]

Strain or genetic background of model organisms

C57BL/6, BALB/c, CD1, Sprague-Dawley

6.6. Optional Columns - Carrier and Multiplexing (SCoPE-MS type methods)

Column Name Description Example Values

comment[carrier proteome]

Description of carrier proteome used in carrier-assisted SCP methods

200x HeLa carrier, 100 ng bovine serum albumin, no carrier

comment[carrier channel]

TMT/iTRAQ channel used for carrier proteome

TMT131C, TMT126, not applicable

comment[reference channel]

TMT/iTRAQ channel used for reference/booster sample

TMT127N, TMT134N, not applicable

6.7. Optional Columns - Batch and Technical Tracking

Column Name Description Example Values

comment[LC batch]

Liquid chromatography batch identifier for batch effect tracking

LC1, LC2, column_A

comment[acquisition date]

Date and time of MS data acquisition (ISO 8601 format recommended)

2022-06-01_18:28:37, 2022-06-01, 20220601

6.8. Optional Columns - Spatial Information

For spatially-resolved single-cell proteomics (e.g., laser capture microdissection from tissue):

Column Name Description Example Values

comment[spatial coordinates]

X,Y coordinates if cells were isolated from a spatial context

X=100;Y=250, X=50.5;Y=120.3, not applicable

comment[tissue section]

Tissue section identifier for spatially resolved single-cell proteomics

section_001, slide_A_section_3

7. Sample Type Classification

The comment[sample type] column is critical for distinguishing the different sample types commonly used in SCP experiments:

Sample Type Description Use Case

single cell

An individual cell isolated for proteomics analysis

The primary analyte in SCP experiments

carrier

Carrier proteome channel (e.g., 200x HeLa lysate) used to boost signal in carrier-assisted methods

SCoPE-MS, SCoPE2, and similar methods

reference

Reference channel used for normalization across runs

TMT-based experiments requiring cross-run normalization

empty

Control well with no cells - used to estimate background and noise

QC for cell isolation efficiency

negative control

Negative control sample to assess technical artifacts

Method validation and QC

bulk control

Bulk sample (many cells) for comparison with single-cell data

Benchmarking single-cell vs. bulk measurements

8. Cell Identifier Annotation

Each cell in a single cell proteomics experiment MUST have a unique identifier. The characteristics[cell identifier] serves as the primary cell-level identifier within the dataset.

8.1. Naming Conventions

Recommended naming conventions for cell identifiers:

  • Well-based: well_A01, plate1_A01, 96wp_A01

  • Sequential: cell_001, cell_002, SC_0001

  • Barcode-based: barcode_ATCGATCG (for methods using molecular barcodes)

  • Combined: plate1_well_A01_cell1

For non-single-cell channels, use descriptive identifiers:

  • Carrier: carrier

  • Reference: reference

  • Empty: empty

8.2. Uniqueness Requirements

  • Cell identifiers MUST be unique within the SDRF file

  • When combining multiple plates/batches, include batch information in the identifier

  • The combination of source name + assay name + comment[label] + characteristics[cell identifier] should uniquely identify each row

9. Multiplexed Single Cell Proteomics Checklist

Many SCP methods use TMT or similar labeling to multiplex multiple cells in a single MS run. For these experiments:

9.1. SCoPE-MS and Similar Methods

source name comment[sample type] characteristics[cell identifier] characteristics[individual] characteristics[cell type] comment[sample preparation batch] comment[label] comment[carrier channel] comment[data file]

PBMC_patient1

single cell

cell_001

patient_001

T cell

plate1

TMT127N

TMT131C

run001.raw

PBMC_patient1

single cell

cell_002

patient_001

B cell

plate1

TMT127C

TMT131C

run001.raw

PBMC_patient1

single cell

cell_003

patient_001

monocyte

plate1

TMT128N

TMT131C

run001.raw

carrier_reference

carrier

carrier

not applicable

mixed

plate1

TMT131C

TMT131C

run001.raw

empty_well

empty

empty

not applicable

not applicable

plate1

TMT126

TMT131C

run001.raw
Sample metadata
Data file metadata

9.2. Carrier and Reference Channels

When using carrier proteome strategies:

  • Use comment[sample type] to clearly indicate which rows are carriers, references, or single cells

  • Annotate the carrier channel with comment[carrier channel]

  • Use characteristics[cell identifier] = carrier, reference, or empty for non-single-cell channels

  • Set characteristics[cell type] to mixed or specify the carrier source for carrier channels

10. Label-Free Single Cell Proteomics Checklist

For label-free SCP methods (nanoPOTS, direct injection, etc.):

  • Set comment[label] to label free sample

  • Each cell corresponds to one MS run

  • Cell identifier still required for tracking

Example:

source name comment[sample type] characteristics[cell identifier] characteristics[individual] characteristics[cell type] assay name comment[label] comment[data file]

tissue_section_1

single cell

cell_LCM_001

donor_A

epithelial cell

LCM_cell_001_run

label free sample

cell001.raw

tissue_section_1

single cell

cell_LCM_002

donor_A

stromal cell

LCM_cell_002_run

label free sample

cell002.raw
Sample metadata
Data file metadata

11. Example SDRF File

A complete example for a TMT-based single cell proteomics experiment aligned with Nature Methods guidelines:

source name comment[sample type] characteristics[cell identifier] characteristics[individual] characteristics[cell type] characteristics[forward scatter] characteristics[side scatter] ... comment[sample preparation batch] comment[label] comment[data file]
PBMC_donor1 single cell cell_T001 patient_001 T cell 316.0 301.0 ... plate1 TMT127N batch1_set1.raw
PBMC_donor1 single cell cell_B001 patient_001 B cell 250.0 184.0 ... plate1 TMT127C batch1_set1.raw
carrier_HeLa carrier carrier not applicable epithelial cell not applicable not applicable ... plate1 TMT131C batch1_set1.raw
empty_control empty empty not applicable not applicable not applicable not applicable ... plate1 TMT126 batch1_set1.raw
Sample metadata Data file metadata
Note
 The …​ column indicates omitted columns (organism, disease, biological replicate, assay name, technology type, carrier proteome, fraction identifier, cleavage agent details, instrument).

12. Quality Control Annotations

For single cell proteomics, the following quality-related annotations are RECOMMENDED:

Column Name Description Example Values

comment[proteins identified]

Number of proteins identified per cell (post-analysis)

500, 1200, 2500

comment[peptides identified]

Number of peptides identified per cell (post-analysis)

1500, 3500, 8000

comment[MS2 identification rate]

Percentage of MS2 spectra resulting in identifications

25%, 40%, 55%

comment[missing value percentage]

Percentage of missing values for this cell

30%, 45%, 60%
Note
 These columns may be added after data analysis and are not required at submission time.

13. Best Practices for Single Cell Proteomics Annotation

Following the Nature Methods SCP guidelines:

  1. Always classify sample types: Use comment[sample type] to distinguish single cells from carriers, references, and controls.

  2. Provide unique cell identifiers: Each cell MUST have a unique identifier enabling tracking through analysis.

  3. Document isolation method: Specify the exact technique used for cell isolation (FACS, LCM, cellenONE, etc.).

  4. Track individual/donor information: Use characteristics[individual] to identify which donor/patient each cell came from.

  5. Record batch information: Document comment[sample preparation batch] and comment[LC batch] for batch effect correction.

  6. Include flow cytometry data when available: Report FSC, SSC, and sorting marker values.

  7. Annotate carrier channels: When using carrier proteome, clearly identify which channels contain carrier vs. single cells.

  8. Include cell type information: Annotate cell types using Cell Ontology terms when possible.

  9. Report cell numbers: Use comment[cells per well] to indicate whether measurements are truly single cells or small pools.

  10. Use factor values: Use factor value columns to indicate experimental variables (treatment, time point, cell type comparisons).

14. Template File

The single cell proteomics SDRF template file is available in this directory:

15. Validation

Single cell proteomics SDRF files should be validated using the sdrf-pipelines tool:

pip install sdrf-pipelines
parse_sdrf validate-sdrf --sdrf_file your_file.sdrf.tsv --template single-cell

16. Authors and Maintainers

This template was developed by the SDRF-Proteomics community with contributions from single cell proteomics researchers, aligned with the Nature Methods SCP consensus guidelines.

For questions or suggestions, please open an issue on the GitHub repository.

17. References

  • Gatto L, Aebersold R, Cox J, et al. (2023). Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments. Nature Methods, 20(3), 375-386. https://doi.org/10.1038/s41592-023-01785-3

  • Slavov N. (2021) Single-cell protein analysis by mass spectrometry. Current Opinion in Chemical Biology.

  • Specht H, et al. (2021) Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Genome Biology.

  • Kelly RT. (2020) Single-cell Proteomics: Progress and Prospects. Molecular & Cellular Proteomics.

  • Budnik B, et al. (2018) SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biology.