Biomarker Discovery in Complex Disease States: Sample Selection Strategies Across Specimen Types

Photo Credit: CDC/ Renelle Woodall

Biomarker discovery is one of the most critical frontiers in translational medicine, bridging basic disease mechanisms and the clinical diagnostic tools that improve patient outcomes. Identifying molecular signatures that predict disease risk, diagnose conditions earlier, stratify patients, or monitor treatment response depends fundamentally on the quality and appropriateness of the biospecimens analyzed.

Human biology is complex, spanning countless molecular species across genomic, transcriptomic, proteomic, and metabolomic dimensions. That complexity demands strategic specimen selection matched precisely to research objectives and analytical platforms.

The specimen type you select profoundly influences which molecular species can be detected, the dynamic range of measurement platforms, and ultimately which biological insights emerge from costly discovery campaigns. Human Plasma offers access to circulating proteins, metabolites, and cell-free nucleic acids released from distant tissues — a liquid biopsy window into systemic disease. Human Serum contains clot-derived proteins absent from plasma, enriching certain analyte classes while depleting coagulation factors. Human PBMCs enable cellular phenotyping, functional assays, and single-cell analyses revealing immune perturbations invisible in acellular biofluids.

At Sanguine, our portfolio spans cellular and fluid matrices, combined with detailed genomic annotation from 70,000+ donors across the United States. This breadth enables researchers to select optimal specimen types matched to their specific biomarker discovery objectives.

Strategic Framework for Specimen Type Selection

The fundamental choice between cellular and acellular biospecimens hinges on where candidate biomarkers reside — in cells, secreted into circulation, or both. Cytokines exemplify molecules existing in multiple compartments: synthesized within cells, secreted into local microenvironments, and detectable in systemic circulation at varying concentrations.[1] Choosing the right specimen type requires understanding the biological compartment where biomarker signal-to-noise ratios maximize detection while minimizing pre-analytical confounders.

Acellular biofluids like Human Plasma and Human Serum offer distinct advantages for circulating biomarker discovery. Proteome complexity stays manageable compared with tissue lysates, easing identification of low-abundance proteins by mass spectrometry or aptamer-based platforms.[2] Standard venipuncture enables longitudinal sampling for monitoring disease progression or treatment response. Cryopreservation maintains protein and metabolite stability for years when properly executed, supporting retrospective case-control studies and biobank-based discovery.

But plasma and serum present challenges. The 10,000-fold concentration range of plasma proteins — from albumin at 40 mg/mL to cytokines at pg/mL — demands depletion strategies or high-sensitivity assays to detect rare species.[3] Pre-analytical variables including tube type, processing delays, centrifugation protocols, and freeze-thaw cycles dramatically affect analyte stability and reproducibility. Distinguishing true disease biomarkers from pre-analytical artifacts requires rigorous quality control and standardized processing.

Cellular biospecimens including Human PBMCs, Human Whole Blood, and Human Leukopak preparations enable complementary approaches. Single-cell transcriptomics reveals cellular heterogeneity and rare populations invisible in bulk analyses.[4] Functional assays identify mechanistic biomarkers reflecting pathophysiology rather than mere associations. Immunophenotyping quantifies cell subset frequencies altered in disease, with demonstrated diagnostic or prognostic value.

Specialized specimens expand discovery beyond peripheral blood. Human Synovial Fluid provides direct access to joint inflammation biomarkers in rheumatoid arthritis and osteoarthritis, with protein concentrations reflecting local disease activity more sensitively than systemic markers.[5] Tissue biopsies enable spatial transcriptomics and proteomics that preserve anatomical context critical for understanding tumor microenvironments or organ-specific pathology.

Plasma vs. Serum: Navigating Critical Selection Criteria

The plasma-versus-serum decision is one of the most consequential choices in biofluid biomarker discovery, and the optimal selection depends on the analyte classes of interest and the analytical platforms used. Human Plasma collected with anticoagulants (EDTA, citrate, or heparin) prevents clotting, preserving coagulation factors and minimizing platelet activation that releases proteins and bioactive molecules.[6] This stability advantage is especially important for cytokine measurements, where platelet-derived factors can artificially elevate serum concentrations above true circulating levels.

Human Serum preparation involves intentional clotting before centrifugation, depleting fibrinogen and other coagulation proteins while enriching growth factors released during clot formation. This introduces biological variability tied to clotting time, temperature, and separation timing.[7] Serum still offers advantages for certain immunoassays optimized against serum reference ranges, and some analytes are more stable in serum than plasma.

Anticoagulant choice adds further complexity. EDTA chelates divalent cations (calcium, magnesium) required for many enzymatic reactions and can interfere with metal-dependent assays.[8] Citrate dilutes specimens by 10%, requiring concentration adjustments. Heparin inhibits PCR at collection-tube concentrations, precluding certain nucleic acid analyses. Matching anticoagulant to downstream platforms prevents costly discovery failures from assay incompatibilities.

Proteomic differences between plasma and serum extend beyond coagulation factors to many proteins differentially abundant due to platelet activation and clotting. Mass spectrometry identifies hundreds of proteins differentially expressed between matched plasma and serum from the same individuals.[9] Candidates discovered in one matrix may not validate in the other — so maintain specimen-type consistency across discovery, validation, and clinical translation.

Pre-analytical standardization often matters more than specimen type. Variation in processing time, centrifugation g-force and duration, storage temperature, and freeze-thaw cycles can introduce more variability than the biological difference between disease and control groups.[10] Sanguine’s standardized protocols minimize these confounders, and comprehensive genomic annotation documents every step from phlebotomy through cryopreservation so researchers can account for remaining variables.

Cellular Biospecimens for Multi-Omics Discovery

Human PBMCs are the cellular workhorse for immune-focused biomarker discovery, offering access to lymphocyte and monocyte populations whose frequencies, phenotypes, and functional states change dramatically in disease. Single-cell RNA sequencing of PBMCs from disease cohorts reveals disease-specific cell states, altered differentiation trajectories, and novel populations rare in healthy controls.[11] These cellular biomarkers often connect signals directly to pathophysiology.

Cryopreservation enables retrospective discovery using biobanked PBMC collections, with hundreds of thousands of annotated specimens from cohort studies and clinical trials. Post-thaw viability above 85% maintains integrity for flow cytometry, bulk RNA sequencing, and many functional assays.[12] Single-cell technologies are more sensitive to cryopreservation artifacts, so distinguishing true biomarkers from freeze-thaw effects requires matched fresh and frozen controls.

Human Whole Blood offers unique advantages for RNA-based discovery, with expression signatures reflecting recent cellular activation and remaining stable for 24 hours in appropriate RNA preservation tubes.[13] Whole blood transcriptomic signatures show diagnostic and prognostic value across infectious diseases, autoimmune conditions, and cancer. Collection simplicity eases translation into point-of-care or clinical laboratory tests.

The cellular heterogeneity of whole blood and PBMCs requires either computational deconvolution to infer cell-type-specific signals from bulk data or single-cell approaches measuring each cell directly.[14] Cell-type proportions vary across individuals and disease states, confounding bulk measurements. Flow cytometry counting or deconvolution algorithms help distinguish altered cell frequencies from per-cell expression changes.

Large-scale collections like Human Leukopak preparations enable cell-type-specific isolation unavailable with standard draws. Magnetic bead sorting, FACS, or density gradient separations enrich specific populations for focused discovery.[15] This targeted approach reduces sample complexity, enriches rare populations, and enables deeper profiling of cell-type-specific alterations driving disease.

Pre-Analytical Variables: The Hidden Determinants of Reproducibility

Pre-analytical variables — the many factors affecting specimens between collection and analysis — are the most common cause of biomarker non-reproducibility. Temperature excursions, processing delays, inconsistent centrifugation, poor storage, or freeze-thaw cycles introduce molecular changes that can exceed disease-related alterations.[16] Candidates discovered using poorly controlled specimens often fail validation in cohorts with different pre-analytical conditions.

Hemolysis — red cell rupture releasing intracellular contents — ranks among the most problematic variables for plasma and serum discovery. Even mild hemolysis (free hemoglobin as low as 0.2 g/L) alters measurements of potassium, lactate dehydrogenase, and aspartate aminotransferase.[17] Proteomic and metabolomic analyses show hundreds of hemolysis-attributable changes, with contaminating intracellular species masquerading as disease biomarkers in insufficiently controlled studies.

Standardized Operating Procedures for collection, processing, and storage are essential infrastructure. The Biospecimen Reporting for Improved Study Quality (BRISQ) guidelines enumerate critical variables to document and control.[18] Minimum documentation includes processing time from venipuncture to cryopreservation, centrifugation parameters, storage temperature, freeze-thaw history, and fasting status. Sanguine tracks all pre-analytical variables with electronic records accompanying every specimen.

Batch effects — systematic technical variation when specimens are processed or analyzed in separate batches — confound discovery when batch assignment correlates with case-control status. Processing all disease samples in one batch and controls in another makes disease and batch effects impossible to separate.[19] Randomized processing, balanced batch designs, and statistical correction all contribute to robust discovery. Our prospective collection services enable batch-balanced designs.

The impact of pre-analytical variables varies by analyte class, requiring class-specific controls. RNA degrades rapidly at room temperature and needs immediate stabilization or cold storage. Proteins are relatively stable but susceptible to proteolysis during delays. Metabolites vary widely, with some altered within minutes.[20] Understanding analyte-specific stability informs specimen selection and acceptable processing timelines.

Critical Pre-Analytical Factors in Sample Selection

Systematic evaluation of pre-analytical parameters protects discovery investments from technical confounders. The following framework guides quality-focused procurement.

Collection Timing and Clinical Context

Disease State Definition:

• Precise disease duration documentation (days since symptom onset)
• Disease stage classification using validated staging systems
• Treatment-naive vs. treatment-experienced status clearly defined
• Comorbidity documentation affecting systemic biology
• Acute exacerbation vs. stable disease state
• Fasting status for metabolic biomarker studies
• Time of day for circadian-influenced analytes
• Medication timing relative to blood draw

Longitudinal Collection Parameters:

• Consistent collection timepoints across subjects
• Baseline pre-diagnosis specimens when available
• Serial sampling intervals matched to disease kinetics
• Paired pre/post-treatment specimens for response biomarkers
• Long-term follow-up specimens for prognostic marker validation
• Matched specimens from flare and remission states
• Collection protocols accounting for diurnal variation
• Standardized interval from last meal for metabolic studies

Processing and Preservation Standards

Temperature Control and Processing Timing:

• Time from venipuncture to processing start (target <2 hours)
• Sample temperature maintenance during transport (2-8°C)
• Centrifugation parameters (g-force, duration, temperature) specified
• Processing environment temperature monitoring
• Time from processing to cryopreservation documented
• Controlled-rate freezing protocols when applicable
• Storage temperature verification with continuous monitoring
• Maximum storage duration before analysis specified

Quality Assessment and Documentation:

• Hemolysis index measurement and acceptance criteria (<0.5 g/L)
• Lipemia assessment via optical measurement
• Icterus quantification when clinically relevant
• Platelet count documentation for plasma specimens
• Cell viability quantification for cellular products
• Bacterial/fungal contamination screening
• pH and osmolality for specialized applications
• Visual inspection with photographic documentation

Aliquoting and Storage Strategies:

• Single-use aliquots preventing freeze-thaw cycles
• Aliquot volumes matched to assay requirements
• Cryovial material compatible with storage temperature
• Freeze-thaw cycle tracking for each aliquot
• Storage location documentation enabling rapid retrieval
• Backup aliquots secured for invaluable specimens
• Storage buffer/media composition documented
• Nitrogen vapor vs. liquid phase specified

Anticoagulant Selection and Effects:

• EDTA for DNA/RNA applications and general proteomics
• Citrate when calcium-dependent processes studied
• Heparin for most plasma protein measurements (avoid for PCR)
• Specialized anticoagulants for specific applications (e.g., ACD for cell separations)
• Anticoagulant concentration verification
• Effects on downstream assay performance validated
• Consistency maintained across all study specimens
• Compatibility with all planned analytical platforms confirmed

Multi-Omics Biomarker Discovery: Quality Requirements Across Technologies

Integrating genomic, transcriptomic, proteomic, and metabolomic data promises comprehensive molecular portraits of disease. But successful multi-omics discovery demands specimen quality meeting the most stringent requirements of any constituent technology. Single specimens feeding multiple platforms eliminate inter-individual variation while requiring careful QC and sufficient volume for all planned analyses.

Transcriptomic discovery using bulk RNA sequencing or microarrays requires RNA integrity numbers (RIN) above 7 for accurate quantification; RIN below 6 introduces systematic bias.[21] Single-cell RNA sequencing is even more sensitive, requiring RIN above 7 and minimal ambient RNA contamination. Our Human Whole Blood collections in RNA preservation tubes maintain RIN scores above 8 under standardized protocols.

Proteomic discovery spans targeted immunoassays and mass spectrometry quantifying thousands of proteins at once, and sample requirements vary widely. ELISA and multiplex bead assays tolerate some pre-analytical variation and can use specimens rejected for mass spectrometry.[22] Discovery-phase mass spectrometry demands pristine specimens with minimal degradation, since proteolytic fragments complicate peptide identification.

Metabolomics is especially sensitive to pre-analytical variables given rapid metabolite turnover and continuing enzymatic activity during processing. Blood glucose declines within minutes at room temperature as glycolysis proceeds; lactate rises during processing delays.[23] Immediate cooling, rapid processing, and enzyme-inhibitor additives minimize these artifacts. Sanguine’s temperature-controlled logistics and documented timelines support confident metabolomic discovery.

Cell-free DNA (cfDNA) is an emerging biomarker class with unique requirements. Its fragmentation patterns, concentration, and sequence composition inform cancer, prenatal testing, and transplant monitoring.[24] Specialized tubes with cellular preservatives prevent white-cell lysis that dilutes the cfDNA signal, and delays as short as 6 hours increase genomic DNA contamination. Our custom protocols accommodate cfDNA-specific requirements for liquid biopsy development.

Essential Quality Parameters for Multi-Omics Studies

Systematic quality assessment across specimen cohorts protects multi-omics investments from artifacts that compromise sensitivity and validation. The following parameters are minimum standards.

RNA Quality and Cellular Integrity Metrics

RNA Preservation and Integrity:

• RIN scores ≥7 for bulk RNA sequencing applications
• RIN scores ≥8 for single-cell RNA sequencing
• 260/280 absorbance ratios 1.8-2.0 indicating protein absence
• 260/230 absorbance ratios >1.8 indicating chemical contaminant absence
• Total RNA yield sufficient for planned experiments plus repeats
• Mitochondrial RNA percentage <20% for scRNA-seq
• Hemoglobin RNA transcripts absent (no hemolysis)
• Ribosomal RNA integrity verified before transcriptome library construction

Cellular Sample Quality Indicators:

• Cell viability ≥85% by trypan blue or flow cytometry
• Doublet rate <5% for single-cell preparations
• Cell size distribution normal for cell type
• Expression of housekeeping genes at expected levels
• Absence of stress response gene activation signatures
• Nuclear integrity for snRNA-seq applications
• Cell membrane integrity by propidium iodide exclusion
• Functional validation in representative specimens

Protein Analyte Stability and Sample Quality

Proteomic Sample Quality Standards:

• Hemolysis index <0.5 g/L free hemoglobin
• Lipemia assessment with turbidity correction if needed
• Total protein concentration within expected ranges (60-80 g/L for plasma)
• Albumin and IgG concentrations normal (primary QC markers)
• Complement activation products absent (C3a, C5a)
• Protease inhibitor cocktails added when proteolytic degradation risks high
• Western blot validation of intact target proteins
• Mass spectrometry peptide coverage for proteins of interest

Pre-Analytical Stability Verification:

• Freeze-thaw cycle limit defined and tracked (<3 cycles typical)
• Storage duration at -80°C documented (most proteins stable ≥2 years)
• Protein degradation assessment via capillary electrophoresis
• Comparison to fresh specimen controls when feasible
• Stability testing for novel biomarker candidates
• Temperature excursion event documentation and impact assessment
• Batch-to-batch reference standard measurements
• Internal standard spike-ins for quality control

Metabolite and Small Molecule Quality Control

Metabolomic Quality Indicators:

• Hemolysis markers (intracellular metabolites) at expected low levels
• Processing time <30 minutes for labile metabolites
• Sample pH within physiological range (7.35-7.45)
• Known stable metabolites at expected concentrations
• Absence of oxidation products in sensitive classes
• EDTA and citrate anticoagulant effects characterized
• Quality control pooled samples for inter-batch normalization
• Metabolite stability validation for specific preservation methods

Cell-Free Nucleic Acid Integrity:

• cfDNA fragment size distributions characteristic (140-170 bp peak)
• Absence of genomic DNA contamination (large fragments)
• cfDNA concentration within expected ranges (<100 ng/mL normal)
• Using blood collection tubes with cellular preservatives
• Processing <6 hours to prevent leukocyte lysis
• Centrifugation optimized to remove cellular debris without cfDNA loss
• cfRNA integrity assessment by bioanalyzer
• Spike-in controls for extraction efficiency validation

Specimen Annotation and Metadata Completeness

Clinical Phenotype Documentation:

• Physician-confirmed diagnoses with ICD-10 codes
• Diagnostic test results supporting phenotype classification
• Disease duration calculated from symptom onset or diagnosis date
• Disease severity scores using validated scales
• Treatment history including doses and administration routes
• Laboratory values proximate to specimen collection
• Imaging results when disease assessment relevant
• Validated patient-reported outcome measures

Matched Specimen Availability:

• Paired specimens from multiple timepoints available
• Matched tissue and blood specimens when applicable
• DNA from same individual for genomic correction
• Family member specimens for heritability studies
• Pre/post-treatment paired collections
• Technical replicate aliquots for reproducibility assessment
• Sufficient volume for discovery and validation cohorts
• Longitudinal specimens spanning disease natural history

Biomarker Discovery Strategies for Hematological Diseases

Hematological disease biospecimens present unique opportunities and challenges, since diseased tissue is accessible via peripheral blood. Leukemias, lymphomas, and myelodysplastic syndromes involve malignant transformation of blood cells themselves, making Human PBMCs, Human Whole Blood, and Human Leukopak preparations ideal for direct tumor profiling.[25]

The clonal architecture of these malignancies — multiple competing clones evolving over time and treatment — demands serial collection capturing disease dynamics. Baseline pre-treatment specimens, on-treatment monitoring samples, and relapse biospecimens enable comprehensive understanding of response and resistance.[26] Biomarkers predicting response, detecting minimal residual disease, and giving early relapse warning are clinically valuable across hematological cancers. Sanguine’s longitudinal collection capabilities support these designs.

Benign hematological disorders — anemia, clotting disorders, immune cytopenias — similarly benefit from blood-based approaches. Plasma and serum contain erythropoietin, coagulation factors, autoantibodies, and other proteins reflecting pathophysiology.[27] Cellular analyses reveal abnormal red-cell morphology, altered platelet function, or perturbed immune development. The challenge is distinguishing primary drivers from secondary compensatory responses — requiring well-phenotyped cohorts.

Inflammatory conditions like hemolytic anemias show why pre-analytical control matters. Disease-related hemolysis releases intracellular contents into plasma/serum, but iatrogenic hemolysis from rough handling produces identical analyte changes.[28] Rigorous hemolysis-index measurement and exclusion criteria prevent mistaking artifacts for biomarkers. Our standardized protocols minimize mechanical hemolysis while measuring free hemoglobin so researchers can exclude compromised specimens.

Genomic alterations drive many hematological malignancies, making DNA from Human PBMCs or Human Leukopak valuable for mutation detection, copy number analysis, and minimal residual disease monitoring. Cell-free DNA from Human Plasma offers non-invasive tumor monitoring as malignant cells apoptose and release mutant fragments.[29] Combining cellular and cfDNA approaches provides complementary information about disease burden, clonal heterogeneity, and response dynamics.

Rare Disease Biomarker Discovery: Navigating Small Sample Challenges

Rare disease biospecimens present unique challenges: limited patient numbers, heterogeneous manifestations, and often undefined mechanisms. Standard power calculations become impractical when entire disease populations number in the hundreds or thousands globally. Alternative designs — n-of-1 studies, longitudinal within-individual profiling, or mechanistic biomarkers grounded in pathophysiology — prove more feasible.[30]

The rarity of specimens from ultra-rare diseases (<1 per million) elevates the importance of quality and annotation. Irreplaceable specimens demand pristine quality to maximize information and prevent wasted analyses. Comprehensive annotation enables matching specimens to appropriate controls and understanding variability within small cohorts.[31] Sanguine’s access to 70,000+ donors includes individuals with rare genetic, metabolic, and orphan disease diagnoses often excluded from other collections.

Multi-omic profiling is particularly valuable in rare disease, where hypothesis-free discovery can identify unexpected pathways and targets. A patient may harbor variants in genes of unknown function; integrated transcriptomic and metabolomic analyses can illuminate downstream consequences.[32] Paired Human PBMCs, Human Plasma, and Human Serum from the same individuals enable these integrative approaches.

Natural history studies benefit from longitudinal collection tracking progression over months to years. Biomarkers predicting complication onset, documenting trajectory, or quantifying treatment response are valuable even when patient numbers preclude large validation cohorts.[33] These mechanistic biomarkers often translate across disease boundaries when common pathways are affected. Custom prospective collection services accommodate the extended timelines and flexible scheduling rare disease studies require.

International collaboration amplifies statistical power but introduces standardization challenges across sites and countries. Harmonized protocols, centralized processing, pre-analytical quality monitoring, and comprehensive metadata enable productive collaboration.[34] Sanguine’s geographic reach across the United States and standardized SOPs position us as centralized collection infrastructure for multi-site rare disease studies.

Quality Control Strategies for Large-Scale Discovery Cohorts

Cohorts spanning hundreds to thousands of specimens demand systematic QC to prevent batch effects and identify problematic specimens before expensive analyses consume material. Pooled “super samples” combining small aliquots from many specimens serve as technical replicates distributed across batches, enabling normalization and batch correction.[35]

Pilot quality assessments using representative specimens from each group identify pre-analytical issues before committing entire cohorts. Measuring standard clinical values (CBCs, chemistry panels), quantifying hemolysis, and assessing RNA integrity in pilots lets researchers anticipate issues and remediate.[36] These checks typically cost <5% of study budgets but prevent catastrophic failures consuming irreplaceable specimens.

Hierarchical clustering and principal component analysis on genome-wide data serve as post-hoc QC, identifying outliers driven by technical artifacts rather than biology. Specimens clustering by batch, processing date, or storage duration rather than disease status flag batch effects needing correction or exclusion.[37] These unsupervised analyses detect issues invisible to conventional QC. Our comprehensive pre-analytical documentation lets researchers annotate molecular data with all technical variables, aiding artifact detection and correction.

Sanguine’s Integrated Biomarker Discovery Solutions

Our end-to-end support spans specimen-selection consultation through delivery of quality-controlled, comprehensively annotated biospecimens meeting the requirements of modern multi-omic platforms. Direct relationships with 70,000+ donors across the United States enable access to hematological disease biospecimens, rare disease biospecimens, and diverse population cohorts supporting generalizable discovery.[38]

Standardized collection and processing minimize pre-analytical variation, while comprehensive genomic annotation documents every variable that could affect quality or molecular readouts. From study design to receipt of samples, our scientific team collaborates with researchers to optimize specimen types, collection strategies, and QC parameters.[39] Custom prospective collection accommodates longitudinal designs, matched-specimen strategies, and specialized processing unique to specific biomarker classes or platforms.

Temperature-controlled logistics maintain cold-chain integrity with real-time tracking. Certificates of analysis documenting all QC accompany shipments, so researchers can exclude problematic specimens or stratify results by quality metrics. Our ISO-certified quality management systems ensure consistency, reproducibility, and regulatory compliance supporting biomarker validation toward diagnostic development.

---

Check Our Inventory

Ready to accelerate your biomarker discovery program with premium-quality biospecimens?

Explore our comprehensive inventory of hematological disease biospecimens and rare disease biospecimens today. Our scientific specialists can discuss your specific biomarker discovery objectives and recommend optimal specimen selection strategies.

Request a Custom Quote →

---

Ethical Sourcing and Regulatory Compliance

All Sanguine biospecimens are collected under IRB-approved protocols with comprehensive informed consent from every donor. Our HIPAA-compliant data management systems protect donor privacy while enabling researchers to access detailed genomic annotation supporting their studies. We maintain ISO 9001:2015 and ISO 13485:2016 certifications demonstrating our commitment to quality management across all operations.

Donor compensation follows ethical guidelines established by professional societies, ensuring voluntary participation without coercion. Geographic diversity in our collection network across the United States supports health equity in research while providing access to underrepresented populations often excluded from biomedical studies. Every specimen is designated Research Use Only (RUO) with clear documentation of its intended application scope.

---

References

1. Pedersen BK, Febbraio MA. Muscles, exercise and obesity: skeletal muscle as a secretory organ. Nat Rev Endocrinol. 2012;8(8):457-465. doi:10.1038/nrendo.2012.49
2. Anderson NL, Anderson NG. The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics. 2002;1(11):845-867. doi:10.1074/mcp.r200007-mcp200
3. Hortin GL, Sviridov D, Anderson NL. High-abundance polypeptides of the human plasma proteome comprising the top 4 logs of polypeptide abundance. Clin Chem. 2008;54(10):1608-1616. doi:10.1373/clinchem.2008.108175
4. Papalexi E, Satija R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol. 2018;18(1):35-45. doi:10.1038/nri.2017.76
5. Lotz M, Martel-Pelletier J, Christiansen C, et al. Value of biomarkers in osteoarthritis: current status and perspectives. Ann Rheum Dis. 2013;72(11):1756-1763. doi:10.1136/annrheumdis-2013-203726
6. Drake SK, Bowen RA, Remaley AT, Hortin GL. Potential interferences from blood collection tubes in mass spectrometric analyses of serum polypeptides. Clin Chem. 2004;50(12):2398-2401. doi:10.1373/clinchem.2004.040303
7. Tuck MK, Chan DW, Chia D, et al. Standard operating procedures for serum and plasma collection: early detection research network consensus statement standard operating procedure integration working group. J Proteome Res. 2009;8(1):113-117. doi:10.1021/pr800545q
8. Cheng S, Netea MG, Joosten LAB. The interplay between central metabolism and innate immune responses. Cytokine Growth Factor Rev. 2014;25(6):707-713. doi:10.1016/j.cytogfr.2014.06.008
9. Hsieh SY, Chen RK, Pan YH, Lee HL. Systematical evaluation of the effects of sample collection procedures on low-molecular-weight serum/plasma proteome profiling. Proteomics. 2006;6(10):3189-3198. doi:10.1002/pmic.200500535
10. Betsou F, Lehmann S, Ashton G, et al. Standard preanalytical coding for biospecimens: defining the sample PREanalytical code. Cancer Epidemiol Biomarkers Prev. 2010;19(4):1004-1011. doi:10.1158/1055-9965.EPI-09-1268
11. Stephenson E, Reynolds G, Botting RA, et al. Single-cell multi-omics analysis of the immune response in COVID-19. Nat Med. 2021;27(5):904-916. doi:10.1038/s41591-021-01329-2
12. Ramachandran H, Laux J, Moldovan I, et al. Optimal thawing of cryopreserved peripheral blood mononuclear cells for use in high-throughput human immune monitoring studies. Cells. 2012;1(3):313-324. doi:10.3390/cells1030313
13. Debey S, Schoenbeck U, Hellmich M, et al. Comparison of different isolation techniques prior gene expression profiling of blood derived cells: impact on physiological responses, on overall expression and the role of different cell types. Pharmacogenomics J. 2004;4(3):193-207. doi:10.1038/sj.tpj.6500240
14. Newman AM, Steen CB, Liu CL, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019;37(7):773-782. doi:10.1038/s41587-019-0114-2
15. Maecker HT, McCoy JP, Nussenblatt R. Standardizing immunophenotyping for the Human Immunology Project. Nat Rev Immunol. 2012;12(3):191-200. doi:10.1038/nri3158
16. Lippi G, Salvagno GL, Montagnana M, Franchini M, Guidi GC. Venous stasis and routine hematologic testing. Clin Lab Haematol. 2006;28(5):332-337. doi:10.1111/j.1365-2257.2006.00818.x
17. Moore HM, Kelly AB, Jewell SD, et al. Biospecimen reporting for improved study quality (BRISQ). Cancer Cytopathol. 2011;119(2):92-101. doi:10.1002/cncy.20147
18. Leek JT, Scharpf RB, Bravo HC, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010;11(10):733-739. doi:10.1038/nrg2825
19. Stevens VL, Hoover E, Wang Y, Zanetti KA. Pre-analytical factors that affect metabolite stability in human urine, plasma, and serum: a review. Metabolites. 2019;9(8):156. doi:10.3390/metabo9080156
20. Schroeder A, Mueller O, Stocker S, et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol. 2006;7:3. doi:10.1186/1471-2199-7-3
21. Geyer PE, Holdt LM, Teupser D, Mann M. Revisiting biomarker discovery by plasma proteomics. Mol Syst Biol. 2017;13(9):942. doi:10.15252/msb.20156297
22. Fliniaux O, Gaillard G, Lion A, Cailleu D, Mesnard F, Betsou F. Influence of common preanalytical variations on the metabolic profile of serum samples in biobanks. J Biomol NMR. 2011;51(4):457-465. doi:10.1007/s10858-011-9574-5
23. Cristiano S, Leal A, Phallen J, et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature. 2019;570(7761):385-389. doi:10.1038/s41586-019-1272-6
24. Döhner H, Weisdorf DJ, Bloomfield CD. Acute myeloid leukemia. N Engl J Med. 2015;373(12):1136-1152. doi:10.1056/NEJMra1406184
25. Grimwade D, Ivey A, Huntly BJP. Molecular landscape of acute myeloid leukemia in younger adults and its clinical relevance. Blood. 2016;127(1):29-41. doi:10.1182/blood-2015-07-604496
26. Weiss G, Goodnough LT. Anemia of chronic disease. N Engl J Med. 2005;352(10):1011-1023. doi:10.1056/NEJMra041809
27. Lippi G, Caputo M, Banfi G, Buttarello M, Cerotti F, Daves M. Recommendations for detection and management of unsuitable samples in clinical laboratories. Clin Chem Lab Med. 2007;45(6):728-736. doi:10.1515/CCLM.2007.174
28. Wan JCM, Massie C, Garcia-Corbacho J, et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer. 2017;17(4):223-238. doi:10.1038/nrc.2017.7
29. Gainotti S, Mascalzoni D, Bros-Facer V, et al. Meeting patients’ right to the correct diagnosis: ongoing international initiatives on undiagnosed rare diseases and ethical and social issues. Int J Environ Res Public Health. 2018;15(10):2072. doi:10.3390/ijerph15102072
30. Boycott KM, Rath A, Chong JX, et al. International cooperation to enable the diagnosis of all rare genetic diseases. Am J Hum Genet. 2017;100(5):695-705. doi:10.1016/j.ajhg.2017.04.003
31. Haendel MA, Chute CG, Robinson PN. Classification, ontology, and precision medicine. N Engl J Med. 2018;379(15):1452-1462. doi:10.1056/NEJMra1615014
32. Austin CP, Cutillo CM, Lau LPL, et al. Future of rare diseases research 2017-2027: an IRDiRC perspective. Clin Transl Sci. 2018;11(1):21-27. doi:10.1111/cts.12500
33. Thompson R, Johnston L, Taruscio D, et al. RD-Connect: an integrated platform connecting databases, registries, biobanks and clinical bioinformatics for rare disease research. J Gen Intern Med. 2014;29(Suppl 3):S780-S787. doi:10.1007/s11606-014-2908-8
34. Dunn WB, Wilson ID, Nicholls AW, Broadhurst D. The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis. 2012;4(18):2249-2264. doi:10.4155/bio.12.204
35. Hewitt SM, Lewis FA, Cao Y, et al. Tissue handling and specimen preparation in surgical pathology: issues concerning the recovery of nucleic acids from formalin-fixed, paraffin-embedded tissue. Arch Pathol Lab Med. 2008;132(12):1929-1935. doi:10.5858/132.12.1929
36. Lazar C, Meganck S, Taminau J, et al. Batch effect removal methods for microarray gene expression data integration: a survey. Brief Bioinform. 2013;14(4):469-490. doi:10.1093/bib/bbs037
37. Henderson GE, Cadigan RJ, Edwards TP, et al. Characterizing biobank organizations in the US: results from a national survey. Genome Med. 2013;5(1):3. doi:10.1186/gm407
38. Coppola L, Cianflone A, Grimaldi AM, et al. Biobanking in health care: evolution and future directions. J Transl Med. 2019;17(1):172. doi:10.1186/s12967-019-1922-3