Skip to content
VSQRD
Log inSign up

Product · By use case

Count the immune cells your study is about.

CAR+ T cells, exhaustion markers and memory subsets from flow and mass cytometry, and immune cells named in place on multiplex slides.

A real run · CODEX, 58 markers

Every cell found and typed, on one section of human small bowel.

This is the workspace from a real run. The AI found 50,264 cells and named 40,168 of them, in 23 types. It left out the rest and says why. Turn the cell types and markers on and off.

B004_Duodenum.ome.tif

The section in grey, by its nuclear stain, Hoechst.The section with CD45 in colour.The section with Vimentin in colour.The section with Vimentin and CD45 in colour.The section with Cytokeratin in colour.The section with Cytokeratin and CD45 in colour.The section with Cytokeratin and Vimentin in colour.The section with Cytokeratin and Vimentin and CD45 in colour.
You asked

This is CODEX multiplex fluorescence of healthy human duodenum. It has 58 markers; Hoechst is the nuclear stain. Find every cell and name its type from this list. The markers say what each type is known by. They are not cuts. Immune: CD8+ T cell (CD3, CD8), CD4+ T cell (CD3, CD4), B cell (CD19, CD21), plasma cell (CD38, CD138), NK cell (CD56 or NKG2D, no CD3), CD7+ immune cell (CD7, no CD3), neutrophil (CD15, CD66, CD16), M1 macrophage (CD68, no CD163 or CD206), M2 macrophage (CD68 with CD163 or CD206), dendritic cell (CD11c, HLA-DR). Epithelium: enterocyte, goblet cell (MUC2), Paneth cell (aDef5), neuroendocrine cell (CHGA or Synapto), cycling TA cell (Ki67, in the crypts), TA cell (SOX9, in the crypts), MUC1+ enterocyte, CD66+ enterocyte, CD57+ enterocyte. Stroma: endothelial cell (CD31, CD34), lymphatic (Podoplanin), smooth muscle (aSMA), nerve (Synapto or PGP95, outside the epithelium), ICC (CD117), stroma (Vimentin, CD90). Name broad kinds first, then the fine kinds inside each. Name debris as debris. Run the blind check and report its agreement. Report the count and percent of each type, and measure the cells layer so the table of every cell is kept.

For you to check
Blind-check agreement only 70%; enterocyte and TA groups blurry

46 of 66 random cells agreed on the first check. The largest disagreement was among weak-marker epithelial cells (enterocyte vs TA cell) and stroma; the check was not repeated after the fixes. Treat enterocyte/TA/stroma counts as approximate.

About a fifth of cells left out; dim-Hoechst cells have no type

9,261 cells were too dim in Hoechst for a nucleus and 374 were bright for most markers; these (the 'left out' group) are unnamed and uncounted. Debris groups (about 460 cells) include all-marker-bright objects that may be B-cell follicle cells.

No M1 macrophage, and 'M2' covers all CD68/CD163/CD206 cells

No group had CD68 without CD163/CD206, so no M1 macrophage was named. Some HLA-DR+CD206+ cells may be dendritic cells. Percent figures are shares of cell area, not of cell number.

Human duodenum, donor B004, CODEX with 58 markers. Slide: Hickey et al., Nature 2023, CC0.

A real run · Flow cytometry, CAR-T

CAR+ T cells in blood after axi-cel, gated against FMO controls.

The request

“These are blood samples from lymphoma patients after axi-cel, a CD19 CAR-T. The CAR is stained in the CAR channel. Use the FMO files in the batch to place each gate. Gate single, live, dump-negative CD45+ CD3+ T cells, then split CD4 and CD8. Report the percent CAR+ among CD3, CD4 and CD8 T cells, and the CD4 to CD8 ratio inside CAR+ cells. Show me every gate.”

What came back, in 10 minutes

31.6%
of T cells are CAR+, 34,218 events.
10.1%
of CD8+ T cells are CAR+.
22.8%
of CAR+ CD4 T cells are Helios+, asked for later in the same chat.
  • A histogram of the CAR channel on T cells, with the cut and the 31.6% above it in orange.

    CAR+ T cells

    The CAR cut, placed from the CAR FMO file.

  • A histogram of the CAR channel on CD8 T cells, with 10.1% above the cut.

    CAR+ CD8 T cells

    The same cut inside the CD8 T cells.

  • A histogram of Helios on CAR+ CD4 T cells, with 22.8% above the cut.

    Helios on CAR+ CD4 T cells

    Cut from its own FMO, to look for CAR+ regulatory T cells.

What it flagged for a person to check

  • CAR does not form two peaks; share depends on the FMO cut
  • CD8 has no FMO; very few CD8+ T cells
  • No CD25 FMO; CD25 line passes below every CAR+ CD4 cell

Blood of a patient 7 days after axi-cel, fluorescence flow. Data: Good et al., Nature Medicine 2022, ODC-PDDL.

A real run · Mass cytometry, CAR-T

Exhaustion and memory on CAR+ T cells, by mass cytometry.

The request

“Gate single live cells, then CD3+ T cells, then CD4 and CD8. Report the percent CAR+ in each. Inside CAR+ CD4 and CAR+ CD8 cells, report the percent positive for PD-1, TIM-3, LAG-3, TIGIT and CD39, and the CCR7 and CD45RA memory subsets. Show every gate.”

What came back, in 7 minutes

29.5%
of CD4 T cells are CAR+, against 14.9% of CD8 T cells.
68.9%
of CAR+ CD4 T cells are PD-1+, and 76.7% TIGIT+.
56.2%
of CAR+ CD4 T cells are CCR7+ CD45RA+.
  • A dot plot of CD4 against CD8, with the CD4 T cells, 51.1%, in orange.

    CD4 T cells

    CD4 against CD8 inside the CD3+ T cells.

  • A histogram of PD-1 on CAR+ CD4 T cells, with 68.9% above the cut.

    PD-1 on CAR+ CD4 T cells

    One of five exhaustion markers, each cut on its own.

  • A dot plot of CCR7 against CD45RA split into four memory subsets, each with its percent.

    Memory subsets

    CCR7 against CD45RA on CAR+ CD4 T cells, in four quarters.

What it flagged for a person to check

  • CAR cut is a judgement; no FMO and no clear negative
  • CCR7 and CD45RA poorly resolved; memory subsets unreliable
  • Marker cuts set on this file only; MFI and cuts need a study-wide rule

Blood at peak expansion after axi-cel, CyTOF. Data: Good et al., Nature Medicine 2022, ODC-PDDL.

A real run · Flow cytometry, 8 colours

Cytokines in stimulated T cells, from an 8-colour panel.

The request

“Gate single cells, live cells, then CD3+ T cells, then CD4+ and CD8+. Report CD4+ and CD8+ as a percent of CD3+. Then, inside CD4+ and inside CD8+ T cells, report the percent positive for IFN-γ, IL-2, TNF and CD107a. Show every gate.”

What came back, in 5 minutes

70.0%
of live single cells are CD3+ T cells.
60.8%
of T cells are CD4+, and 32.7% are CD8+.
2.9%
of CD8+ T cells make IFN-γ after stimulation.
  • A histogram of CD3 on live cells with two peaks, and 70.0% above the cut.

    CD3+ T cells

    Cut in the valley between the two peaks.

  • A dot plot of CD4 against CD8, with the CD4+ T cells, 60.8%, in orange.

    CD4+ T cells

    CD4 against CD8 inside the T cells.

  • A histogram of IFN-γ on CD8+ T cells, with 2.9% above the cut.

    IFN-γ in CD8+ T cells

    The cut set on the unstimulated well of the same donor.

What it flagged for a person to check

  • Unstimulated background not read; response is raw
  • IL-2 is a smear with no clear positive peak
  • CD8 CD107a cut very high; true positives likely missed

One donor's stimulated well, 8-colour intracellular cytokine stain. Data: FlowKit (Scott White), BSD 3-Clause.

What you can ask for

Ask for these, or anything else you can see.

In plain words. If it can be seen on the sample, the AI can build a method to measure it.

  • Percent CAR+ among CD4 and CD8 T cells
  • Exhaustion markers such as PD-1, TIM-3, LAG-3, TIGIT and CD39
  • Memory subsets from CCR7 and CD45RA
  • Intracellular cytokines after stimulation
  • T cells, B cells, plasma cells and macrophages named on multiplex slides

How it works

You upload, and we run the study.

  1. 01

    Upload your slides or flow files

    Any stain or panel. One sample is enough to start.

  2. 02

    Say what to measure

    In plain words, the way you would ask a colleague.

  3. 03

    We run the study

    The AI builds the method on one sample. A person checks it and locks it, and code measures every sample with it.

  4. 04

    Get the report

    Numbers, outlines, gates and flags for every sample. You pay for each slide or flow file.

Why VSQRD

  • Every reported number measured by code, never by eye
  • First-hand research and wet-lab perspective
  • Direct founder involvement from discovery to rollout

Start with one study you have already reported.

Upload its samples and tell us what you measured. We run it and show you where our numbers and yours differ.

Book a Discovery Session