When working with precious samples that may have less than ideal sample viability or quality, meaningful and rich single-cell multiomics data is still achievable. Explore our poster or read below to discover how BioLegend and Q² Solutions collaborated to demonstrate this on clinically relevant Human cryopreserved samples.

Cellular Indexing of Epitopes and Transcriptomes by Sequencing (CITE-seq) is a recent technique that has seen wide adoption in the research communities. However, technical guidance as well as vendor support on the use of the technique along with associated reagents and platforms has been limited when experimental conditions are not ideal regarding sample quality (age, viability, cell recovery, etc). Non-ideal sample conditions are best avoided where practical, however, the reality of clinical research as well as the limitation of accessibility to disease or population-relevant sample types may limit the control an investigator has over some of these sample attributes in practice.

Q² Solutions and BioLegend engaged in a strategic collaboration to demonstrate the rigor and reproducibility of Q² Solutions research service offerings on the 10x Genomics product ecosystem using two common human sample types of high clinical relevance for human disease: cryopreserved Peripheral Blood Mononuclear Cells (cPBMCs) and cryopreserved Bone Marrow Mononuclear Cells (cBMMCs). Several matched samples from three donors for both cPBMCs and cBMMCs were acquired and run in duplicate at both BioLegend and Q² Solutions. All samples were stained using BioLegend TotalSeq™-C Universal Antibody Cocktails (PN 399905) and additional TotalSeq™-C antibodies against human CD235ab, CD90, CD34, CD10, CD135, CD117, CD206, CD271, CD70, CD9, CD200, and, CD74.

cBMMCs can be technically challenging to work with, especially if specimen harvesting and processing is originating in a community setting, and, archival samples are several years old. cBMMCs were processed using identical procedures across both physical locations to investigate intra-site reproducibility.

cPBMCs on the other hand are routinely collected across clinical activities in the research setting. Very high-quality cPBMCs can be obtained, however, quality can be highly variable depending on source, specimen, and, handling (storage, time, operator, etc). Thus, simulated low-quality and high-quality cPBMCs were created by temperature shocking ½ of the matched samples between study sites.

Figure 1 – Study design. All six donors were run at both study sites, in duplicate. All practices were matched except for the introduction of a “cold shock” in the PBMCs at the Q² study site to simulate reduced sample quality.

Selected Results and Conclusions

Cross-run precision (2 replicated per sample) between the frequency of each gated cell type was very high with an average R^2 value >0.985 for cPBMCs and >0.965 for cBMMCs at both locations indicating a very high degree of technical repeatability between replicates. Additionally, the average R^2 value between matched cBMMC samples at each location was 0.963 supporting excellent reproducibility across locations, equipment, and, operators.

cBMMCs as expected had a lower cell viability before loading the 10x Chromium, and, relatively lower 10x Cell Ranger antibody metrics compared with cPBMCs (only reads useable shown to illustrate). Notably, the samples that underwent the temperature shocking method had markedly reduced cell viability and reduced 10x Cell Ranger antibody metrics.

In samples with notably reduced viability, and, comparatively lower 10x Cell Ranger antibody metrics, shifts in major cell-type frequencies were observed as well as reduced staining index across antibody derived tags (ADTs). However, and, importantly adequate cell-type discriminatory sensitivity was retained in these samples to investigate a number of cell types in traditional bi-variate gating.

Below are exemplary plots between the normally processed and lower-quality simulated clinical cPBMCs to demonstrate differences in cell type percentages as well as staining-index differences observed in the matched samples between protocols. Overall while it is strongly advised to strive for the highest quality samples possible to maximize data quality as well as minimize changes in the biological state of cells, high-quality data can still be obtained on samples with much lower sample quality than may typically be advised by vendors providing technical guidance in this space. These data suggest that CITE-seq using TotalSeq products is suitable for clinical samples of varying quality with proper handling and expertise; potential impacts on data interpretation and results should be considered.

Figure 3 – Selected bi-variate plots demonstrating reduced staining index (SI) for some ADTs in the “shocked” cPBMCs as well as an observable shift in measurable T cell memory by CD28, CD95, CD45RA, and CD45RO.

When working with precious clinical samples with expected variable sample quality please contact BioLegend and/or Q² Solutions technical experts for guidance on experimental design and considerations around data quality.

Exemplary cell-type gating and tSNE plots

Shown below are representative filtering and gating approaches used to establish cell types representative of major canonical cell populations in each sample type. Additionally, pseudo-color tSNEs are shown for some protein markers of major cell populations in the blood as well as associated highly differentially expressed RNAs correlated with these same major cell populations.

Figure 4 – Filtering and ADT gating scheme used to establish cPBMC cell types based on canonically understood leukocyte phenotypes in human blood.

Figure 5 – Filtering and ADT gating scheme used to establish cBMMC cell types based on mature leukocyte phenotypes and basic stem/progenitor lineages in human bone marrow.

With highly dimensional, multi-modal data; it is useful to visualize analytes and cell types in both low-dimensional (histograms and bi-variate plots) and high-dimensional space. Depicted below are some sample tSNE (t-distributed Stochastic Neighbor Embedding) plots for a single normally processed PBMC donor (two replicates included) visualizing both cell types established through the previous gating schemes as well as selected ADTs and RNAs.

Figure 6 – tSNE plot colorized based on each of the established cPBMC cell types depicted in figure 4.

Figure 7 – tSNE plots colorized based on selected protein (ADT) and RNA expression for canonical protein markers of cell types and biological state. RNAs were selected based on the expression distribution of ADTs in rows one and three. Cognate mRNA with a protein may not always be detectable, or, specific to a cell type. In some cases, other valuable mRNA molecules were selected to align with a given RNA.