Cell Granularity Reflects Immune Cell Function and Enables Selection of Lymphocytes with Superior Attributes for Immunotherapy

Abstract In keeping with the rule of “form follows function”, morphological aspects of a cell can reflect its role. Here, it is shown that the cellular granularity of a lymphocyte, represented by its intrinsic side scatter (SSC), is a potent indicator of its cell state and function. The granularity of a lymphocyte increases from naïve to terminal effector state. High‐throughput cell‐sorting yields a SSChigh population that can mediate immediate effector functions, and a highly prolific SSClow population that can give rise to the replenishment of the memory pool. CAR‐T cells derived from the younger SSClow population possess desirable attributes for immunotherapy, manifested by increased naïve‐like cells and stem cell memory (TSCM)‐like cells together with a balanced CD4/CD8 ratio, as well as enhanced target‐killing in vitro and in vivo. Altogether, lymphocyte segregation based on biophysical properties is an effective approach for label‐free selection of cells that share collective functions and can have important applications for cell‐based immunotherapies.


Cell granularity reflects immune cell function and enables selection of lymphocytes with superior attributes for immunotherapy
Tongjin Wu, Joel Heng Loong Tan, Wei-Xiang Sin, Yen Hoon Luah, Sue Yee Tan, Myra Goh, Michael E. Birnbaum, Qingfeng Chen,* Lih Feng Cheow*                 and TEMRA subsets (f) under automatically adjusted gating threshold.For each cell population, shown are two representative scatter plots corresponding to 10% and 60% yields of desired cells, and the correlation plots between purity and yield.An average enrichment fold increase from ~100% yield to ~5% yield (n = 3) is indicated.The percentage of desired cells (yield) is calculated as the number of desired cells within the gating to that of total desired cells among the whole cell population.The purity indicates the proportion of desired cells to that of total cell numbers within each gate.

Figure S1 |
Figure S1 | Gating methods to identify main lymphocyte populations and T-cell subsets.a) Gating strategies to identify CD3 + CD4 + T, CD3 + CD8 + T, NK and B cells.b) Characterization of T cell naïve/memory lineages including TN, TCM, TEM, and TEMRA based on the expression of canonical markers CCR7/CD45RA/CD45RO.The blue line represents CCR7/CD45RA isotype gate.

Figure S2 |
Figure S2 | Gate selection based on SSC intensity and composition change of lymphocyte subpopulations.a) Representative gating where 40% of the total lymphocytes with low SSC intensity were selected.The total CD4 + T, CD8 + T, or their refined cell subsets were superimposed onto the gating plot to show their scattering distribution within and outside the gate.b-j) Percentages of CD3 + CD4 + T, CD3 + CD8 + T, NK, and B cells (b-d), or T cell naïve/memory subsets in CD3 + CD4 + T (e-g) and CD3 + CD8 + T cells (h-j) among the gated SSC low cluster (n = 3).

Figure S3 |
Figure S3 | FSC is less efficient in segregating lymphocyte subpopulations.a-c) PBMCs were pre-stained with lineage (a,b) or differentiation-specific markers (c).A gating method based on the FSC intensity was used to divide total lymphocytes into FSC hi and FSC low clusters as illustrated in the dot plots, correspondingly.The percentage of lymphocyte subpopulations within the FSC hi or FSC low cluster was summarized (n = 3).*p < .05,**p < .01,paired two-tailed t-test.

Figure S4 |
Figure S4 | SSC for segregating lymphocyte subpopulations.a) Pre-stained PBMC was analyzed under different voltages for FSC and SSC data acquisition.b) Control cells were unstained but subjected to the same sample processing steps.Shown is the scattering profile of total lymphocytes with or without antibody staining.c,d) The percentage of CD3 + CD4 + T, CD3 + CD8 + T, NK, and B cells in gated SSC low cluster (c), as well as the naive/memory T-cell subsets in CD4 + T and CD8 + T lymphocyte among the gated SSC low cell population (d) were compared under different voltages during data acquisition.Two-way ANOVA test was performed and no significant difference (p = 0.999) was observed between the cell composition as recovered from the sorting under different voltages.

Figure S5 |
Figure S5 | In-depth characterization of phenotypes and functionality of enriched younger T-cell subsets.a) Expression profiles of selected phenotyping markers in sorted SSC high and SSC low cells.Each dot represents data of one donor sample (n = 2).b) Sorted SSC high and SSC low cells were stimulated with PMA plus ionomycin for 4 hours before intracellular staining.Shown are the multifunctional cytokine expression of CD3 + CD8 + T, CD3 + CD8 -T (CD4 + T) cells, and CD3 -(Non-T) cells within sorted SSC high or SSC low cells (n = 2).

Figure S6 |
Figure S6 | Dynamic phenotypic change of individual lymphocyte samples sorted by SSC.a) Gating strategy for in-depth analysis of compositional change of naïve/memory T cells over expansion course.b-d) Composition change of naive/memory T-cell subsets in CD3 + CD4 + T or CD3 + CD8 + T cells for each donor sample along the expansion procedure.e) T cell naïve/memory composition at day 19 post-expansion.Error bars indicate mean ± SEM (n = 2).

Figure S8 |
Figure S8 | In-depth phenotypic analysis of expanded CD4 + T and CD8 + T cells.a-d) Longitudinal assessment of phenotypic change of TCMRA-hi compared to other T cell subsets.Average expression and abundance per cell of selected phenotypic markers within CD4 + T (a,b) or CD8 + T subsets (c,d) (n = 3).The relative fluorescence intensity (RFI) was reported after normalization to that of TCMRA-hi of the same time points respectively (b,d).The cross label indicates omitted data due to low cell percentages for marker expression identification or insufficient counts for MFI calculation.

Figure S9 |
Figure S9 | Study of CD4 + T help in regulating CD8 + T proliferation and function.a) Synchronized T cell expansion potency with the ratio of CD4/CD8 T cells starting from cell materials of different SSC intensity.Data are mean ± S.E.M (n = 3).b) CD4 + cells and CD8 + cells were sorted and validated to contain more than 96% of CD3+ CD4+ T and CD3 + CD8 + T cells, respectively.CD4 + cells and CD8 + cells were reconstituted at indicated ratio and coexpanded by CD3/CD28 engagement.

Figure S10 |
Figure S10 | Expression profiles of selected phenotypic markers in co-expanded T cells.a,b) Dynamic expression of a panel of phenotypic markers in CD3 + CD4 + T (a) or CD3 + CD8 + T cells (b) at day 6, 11, and 18 after CD4-CD8 T co-expansion.The relative fluorescence intensity (RFI) was calculated by normalizing the medium fluorescence intensity (MFI) of each marker under CD4-CD8 co-culture conditions to that of CD4 + T cells or CD8 + T cells without co-culture at the indicated time points.

Figure S11 |
Figure S11 | Representative CAR expression over time.

Figure S13 |
Figure S13 | Comparable target-killing activity of different CAR-T cells at high E-T ratio.a,b) CAR-T and NALM6.Luc cells were co-cultured in 96-well round-bottom culture plates at a ratio of 3:1.Killing efficiency was calculated according to the reduction of luciferase intensity in the presence of CAR-T compared to NALM6.Luc cells alone (a).CAR-T numbers on day 3 after co-culture were manually counted (b).

Figure S14 |
Figure S14 | Distinguishable target-killing activity of different CART19 cells at low E-T ratio.CAR-T and NALM6.Luc cells were co-cultured in 24-well flat-bottom culture plates at low E:T ratio as indicated.Representative scatter plots showing the percentage of remaining CAR-T and NALM6.Luc cells at day 3 after co-culture.

Figure S15 |
Figure S15 | Tumor growth over time.Quantification of tumor burden from week 0 to week 4 by measuring total photon flux per second and shown were data of each mouse at individual time points.Data are mean ± S.E.M, unpaired two-tailed t-test.*p < .05,**p < .01.Exact pvalues for moderate but non-significant trends were also indicated.

Figure S16 |
Figure S16 | Post-hoc test following ANOVA analysis.For the comparison of tumor growth over time between different mice groups.Two-way ANOVA analysis with Sidak's multiple comparisons test was performed.Shown were the results of post-hoc test along with the ANOVA test in Figure 8c.*p < .05,**p < .01,***p < .001,****p < .0001.

Figure S17 |
Figure S17 | Computational gate search for T-cell subpopulation selection.The raw values corresponding to FSC-A and SSC-A parameters were exported and reanalyzed by the GateID algorithm to select the desired T-cell subpopulations.Shown are the enrichment efficiency of CD3 + CD4 + T (a), CD3 + CD8 + T (b), total TN (c), TCM (d), TEM (e),