Blood handling is a major source of variance,
so stabilization starts at the draw.
In multi-center trials and distributed cohorts, unstabilized EDTA or heparin collections undergo ex vivo leukocyte gene activation and mRNA degradation within hours of venipuncture.2,3 We standardize collection at the point of draw.
- EDTA and heparin tubes drift ex vivo
- Standard clinical tubes keep cells alive in suspension. As leukocytes sit at room temperature, stress-response and NF-κB signaling transcripts are induced, and hundreds to thousands of genes shift before RNA is extracted.2,3 Delayed centrifugation or variable transit times between sites add technical batch artifacts.
- PAXgene tubes stabilize RNA at the draw
- The tube’s reagent lyses cells, precipitates nucleic acids and inactivates RNases as blood enters the tube. In the original validation, PAXgene eliminated or markedly reduced ex vivo gene induction and RNA degradation over 5 days at room temperature, allowing ambient multi-site shipping.1
- Protocol-trained mobile phlebotomy
- In-home and workplace venipuncture for remote participants in decentralized studies, with standardized tube inversion and transit logging.
- Pre-labeled kits for study sites
- Barcoded PAXgene collection supplies, packaging and prepaid return shippers, delivered in bulk to study coordinators.
- Chain-of-custody tracking
- Electronic sample logging on arrival at the laboratory, with automated notification and biobanking accession.
38 pathways, six readings and the immune cell mix,
each placed against a reference group.
The Inflammation Compass summarizes gene expression into 38 pathways, 21 for Activation and 17 for Resolution. A statistical model with fixed weights combines them into six readings, and the immune cell mix is estimated from the same data. There is no composite or overall score.
- The six readings
- Each reading is a principal component of the pathway scores within one phase, Activation PC1 to PC3 and Resolution PC1 to PC3, calculated with fixed weights. The Activation readings are Activation Level, Targeted Defense and Where the Fast Response Is Working; the Resolution readings are Resolution Level, Regulatory Cells & Repair Signals and Clean-up & Rebuilding. Five are level readings; Where the Fast Response Is Working is a balance between two sets of pathways. The readings are statistical summaries, not six separate body functions, and a pathway can contribute to more than one.
- Immune cell mix
- Cell-family fractions are estimated by deconvolution of the same sequencing data,4,5 so changes in composition can be read apart from changes in activation state.
- Reference range
- Each result is placed against a reference group of people whose blood was collected, sequenced and analyzed the same way. The reference range is the middle 80 percent of that group, on the reference scale, where 1 is the usual spread between people in the group. A result is within, at the edge, below or above. The group is accruing, and ranges are recalculated as it grows.
- Change between samples
- A reading is marked as moved when the shift exceeds the change threshold, set on the reference scale above the shift observed between back-to-back samples from the same people; otherwise it is about the same. The change threshold and the margin for “at the edge” are provisional and stated on the report. A consistent trend shows direction, not cause.
Where whole-blood RNA fits a study,
as an exploratory measure.
Whole-blood transcriptomics reads inflammatory programs inside circulating immune cells, which a single protein assay doesn’t show. These uses are exploratory: the pathways’ response to interventions is biologically expected but not yet demonstrated.
- Pharmacodynamic secondary endpoints
- Describe systemic inflammatory changes in metabolic, cardiovascular and autoimmune trials. Where hs-CRP is an indirect liver readout, whole-blood RNA reads the programs in circulating immune cells, pathway by pathway: innate sensing, IL-1 signaling, neutrophil and monocyte deployment, platelet activation and the interferon response.
- Cohort characterization
- Describe baseline inflammatory programs across a cohort, for example separating participants whose Activation Level and Resolution Level both sit above their reference ranges from those whose Activation Level alone sits above.
- Repeated measures across a study
- Sample at fixed study timepoints (for example Day 0, Week 4, Week 12 and Month 6) and compare each follow-up with the participant’s baseline for the direction and size of change, including in resolution pathways. Pathway scores are computed within each sample by rank, which makes them robust to run-to-run variation.
- Mechanism-of-action studies
- Measure whether inflammatory programs in circulating immune cells shift after an investigational compound, peptide or lifestyle protocol, and pair transcriptome-wide readouts with deconvolution to separate activation-state changes from estimated shifts in cell composition.
Analytical specifications,
with the reason for each.
Every sample is sequenced and processed the same way, on a versioned pipeline.
- Sequencing platform
- Illumina NovaSeq, 2 × 150 bp paired-end
- High base accuracy (≥85% of bases at Q30 or above for 2 × 150 bp runs) and paired reads long enough to span splice junctions.
- Sequencing depth
- ~30,000,000 read pairs per sample
- Coverage deep enough to detect low-abundance immune receptor transcripts.
- Globin and rRNA depletion
- Before library construction
- Removes abundant hemoglobin (HBA1, HBA2, HBB) and ribosomal transcripts to maximize informative leukocyte coverage.
- Library preparation
- Stranded libraries with DNA removal and ERCC spike-in controls
- Preserves transcript strand, removes genomic DNA carryover, and provides external controls for run-to-run performance.
- RNA quality control
- RNA integrity and quantity checked on every specimen
- Confirms RNA integrity before library construction.
- Alignment and quantification
- STAR alignment to GRCh38, featureCounts gene-level counts, TPM normalization
- Splice-aware alignment and standard gene-level quantification, compatible with common downstream tools.
- Pathway framework
- 38 pathways (21 Activation, 17 Resolution) drawn from curated whole-blood transcriptional modules6 and pathway databases
- Drawn from the published blood transcriptional module framework and confirmed in Biomeme’s internal collection of ~2,500 blood transcriptomes.
- Pathway scoring
- Within-sample mean percentile rank of pathway genes
- Each sample serves as its own reference, making results robust to run-to-run variation and suited to repeated measurement.
- Readings
- Principal components PC1–PC3 within each phase, with fixed weights
- Fixed weights keep the calculation the same for every sample, so readings can be compared across samples and people.
- Cellular deconvolution
- Circulating immune cell types4,5
- Computational estimates of cell composition, used to separate gene expression changes from composition shifts.
- Reference range
- Middle 80% of a reference group measured the same way
- Places each result as within, at the edge, below or above; thresholds are provisional and stated on the report.
Raw files and expression matrices,
delivered to your team.
Research teams receive primary sequencing files, aligned reads and normalized expression matrices through a secure cloud bucket, with chain-of-custody records.
- FASTQ files
- Demultiplexed paired-end raw reads with complete Phred base quality values and FastQC summary reports.
- BAM or CRAM files
- STAR-aligned, indexed alignments, suitable for secondary variant or alternative-splicing analyses.
- Raw counts and TPM
- Gene-level raw count tables formatted for DESeq2, edgeR and limma-voom, plus TPM matrices for visualization and cross-sample comparison.
- Pathway, reading and cell matrices
- Within-sample pathway results, the six readings, cell-fraction estimates and change between samples.
Quality checks on every sample,
and validation status stated exactly.
Biomeme Labs operates a high-complexity laboratory in Philadelphia, PA. Study workflows follow documented standard operating procedures, and a sample that fails a quality gate is re-processed or re-collected and never reported.
- Input RNA integrity
- Spectrophotometric quantification with automated capillary electrophoresis. Every specimen must pass RNA integrity and quantity checks before library construction.
- Library validation
- Fluorometric quantification and fragment size profiling. Equimolar pooling and qPCR quantification prevent lane under-clustering or read imbalance across study participants.
- Run-to-run monitoring
- ERCC RNA spike-in controls are added to every library to monitor technical performance across runs, and pathway scores are computed within each sample by rank.
Validation status
- IRB approval to enroll participants
- Done
- Per-sample quality gates for RNA integrity, read depth, mapping rate, spike-in linearity and contamination
- In place
- Reference group
- Accruing
- Pipeline check against public blood transcriptome datasets
- Sign-off pending
- Within-person and pre-analytical variability study
- Planned
- Samples from known inflammatory conditions, through biobank partners
- Planned
What the measurement
can't show.
The measurement is RNA in whole blood at a single time point. Reading names are an inference about what those signals are associated with, readings are model summaries, and a pathway can feed several. Blood shows some parts of the inflammation system only partly and can’t show where in the body inflammation is taking place, or what caused a result or a change. Deconvolved cell fractions are estimates, and rare subsets carry more estimation variance. Results vary with time of day, recent activity, illness, sleep, stress and hydration.
By construction, about 20 percent of the reference group falls outside any one reference range. The Inflammation Compass is a laboratory-developed test with a general wellness intended use; it is not FDA cleared or approved and does not diagnose, rule out or predict any disease.
Engagement models for academic
and commercial investigators.
From pilot cohorts to multi-year trial contracts, with ~1 month batch turnaround from our CLIA-certified Philadelphia laboratory.
- Pilot feasibility cohorts
- 10 to 50 samples to test transcriptomic endpoints and estimate preliminary effect sizes for grant applications (NIH R01, SBIR/STTR) or IND filings.
- Clinical trial central laboratory
- Contracted central laboratory testing for prospective Phase I–III interventional trials, including study-specific kit assembly, multi-site distribution and chain-of-custody data packages.
- Bioinformatics collaboration
- Work directly with our computational biology team on custom gene module development, cohort trajectory modeling, multi-omic integration and publication support.
Plan the collection protocol before the first draw.
Talk with our genomics and computational biology team about collection protocols, bioinformatic endpoints or a pilot cohort.