Single-cell RNA sequencing of 76,535 capillary PBMCs from 3 donors across 28 samples, freshly isolated or on ice for 24h with manual 4-layer annotation hierarchy

<p><strong>Sample Procurement</strong></p> <p>Samples were collected directly from participants as part of ImYoo&#39;s &quot;Single-cell immune profiling from self-collected capillary blood&quot; study, approved by Advarra IRB (Protocol #Pro00057361).</p> <p><strong>Sample Processing</strong></p> <p>Whole capillary blood samples were self-collected from participants using the <a href="https://yourbiohealth.com/en-us/virtually-painless-blood-collection-devices-for-clinical-trials-and-wellness-testing">TAP II</a> device. Samples were either processed shortly after collection, or if being stored for longer than 4 hours, were kept in a styrofoam cooler with ice packs. Cells were isolated using <a href="https://www.stemcell.com/products/easysep-direct-human-pbmc-isolation-kit.html">EasySep Direct Human PBMC Isolation Kit</a>&nbsp;(STEMCELL Technologies Catalog #19654) and cryopreserved using <a href="https://www.stemcell.com/cryostor-cs10.html">CryoStor CS10</a> (STEMCELL Technologies Catalog #07930). Upon thawing, samples were labeled in accordance with the MULTI-seq&nbsp;protocol (<a href="https://www.nature.com/articles/s41592-019-0433-8">https://www.nature.com/articles/s41592-019-0433-8</a>) and then processed on a 10X Genomics Chromium, using either the Chromium Next GEM Single Cell 3&#39; Kit v3.1 (10X Genomics Product Code&nbsp;1000269) or&nbsp;Chromium Next GEM Single Cell 3&rsquo; HT Kit v3.1 (10X Genomics Product Code 1000370). DNA libraries were sequenced on either a NovaSeq 6000 or NextSeq 550.</p> <p><strong>Data Processing</strong></p> <p>Transcriptomic sequencing data was processed using Cell Ranger v7.0.1 with default parameters. Multiplexing oligo sequencing data was processed through a custom python script that counts the number of occurrences of each sample barcode sequence&nbsp;and assigns it to the corresponding cell barcode. Samples were demultiplexed using a custom algorithm that estimates the background sample barcode counts, and assigns each cell a probability of belonging to each sample. Cell typing was done as part of a larger dataset and consisted of iterative manual assignments of clusters to cell types. For each cell subtype detected, a new model was trained on just the cells of that type, and the process was repeated.</p> <p><strong>Metadata Fields</strong></p> <ul> <li><strong>barcode:</strong> Original chromium cell barcode</li> <li><strong>Sample IDs:</strong> Unique ID for the experimental sample that was processed with 10x Chromium, could have come from the same biological sample (identified by <strong>original_sample_id</strong>)</li> <li><strong>Participant IDs:</strong> Unique ID for participant (here there are three participants: 2, 3 and 51)</li> <li><strong>Cell Barcoding Runs:</strong> Unique ID for the 10x Chromium cell barcoding run in which that sample was processed. Multiple samples can be processed in a cell barcoding run.</li> <li><strong>Lane:</strong> ID of which Chromium chip lane the cell came from</li> <li><strong>extraction_protocol:</strong>&nbsp;How the PBMCs were isolated from whole blood. In this dataset all samples were processed with the TAP device.</li> <li><strong>sample_processing_delay_seconds:</strong> The amount of time (in seconds) between when the blood was extracted from the participant and when PBMC isolation + cryopreservation was performed</li> <li><strong>cell_barcoding_delay_days:</strong> How long PBMC samples were stored in liquid nitrogen&nbsp;prior to being thawed and processed on 10x</li> <li><strong>cell_barcoding_protocol</strong>: Which single cell RNA sequencing experimental protocol was used. Here all samples were processed with 10x v3.1 chemistry.</li> <li><strong>run_lane_batch:</strong> Concatenation of columns <strong>Cell Barcoding Runs</strong> and <strong>Lane</strong> to provide a unique ID for experimental processing batch (i.e. the DNA library)</li> <li><strong>cell_type_level_1:</strong> Level 1 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.</li> <li><strong>cell_type_level_2:</strong> Level 2 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.</li> <li><strong>cell_type_level_3:</strong> Level 3 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.</li> <li><strong>cell_type_level_4:</strong> Level 4 of a 4-tier PBMC ontology that does not provide a label for low quality cells - those were left as NaNs.</li> <li><strong>c1:</strong> Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.</li> <li><strong>c2:</strong> Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.</li> <li><strong>c3:</strong> Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.</li> <li><strong>c4:</strong> Level 1 of a 4-tier PBMC ontology that also provides label for low quality cells, such as Debris, Doublets, experimental artifacts and others. These additional labels can be used for creating a junk detector.</li> <li><strong>original_sample_id:</strong> Some samples were derived from the same originating whole blood sample. This field specifies the source of the whole blood sample.</li> </ul>