Clinical Chemistry

Expanding access to cellular immune analysis is essential for decentralized clinical care, clinical trials, and population-based research. However, current flow cytometry workflows require rapid processing of fresh blood, proximity to a centralized laboratory, and cold chain logistics. Although dried blood spots (DBS) have transformed decentralized molecular diagnostics, no comparable approach has enabled robust flow cytometric analysis of immune cells. Here, we present FlowSpot, a novel platform that enables recovery of leukocytes from DBS and preserves their immunophenotypic characteristics, allowing downstream flow cytometric analysis following ambient-temperature storage and shipment. FlowSpot recovers intact leukocytes while preserving immune cell subset frequencies with strong concordance to fresh whole blood. We demonstrate its clinical utility by enabling remote CD4 T cell immunophenotyping in people living with HIV, showing high agreement with routine clinical measurements across a broad range of CD4 T cell frequencies. Beyond cellular phenotyping, FlowSpot extends immune monitoring to functional profiling by enabling detection of intracellular cytokine responses, including IFN{gamma}, IL-2, and TNF production by CD4 and CD8 T cells following ex vivo PMA/ionomycin stimulation. By overcoming a longstanding barrier to leukocyte recovery from DBS, FlowSpot extends flow cytometry beyond specialized laboratories, expanding access to cellular immune analysis for clinical care, decentralized clinical trials, and population-scale immunology.

Background: We propose the biological drift framework for the interpretation of biological test results: a z-score-like framework based on optimized and personalized reference populations and a distance-to-optimum drift metric for longitudinal interpretation relative to an estimated individual optimum. We benchmarked biological drifts against Reference Change Values (RCVs), which are used to interpret serial laboratory results by defining the minimum change expected to exceed normal within-subject biological variation CVi. Objectives: To benchmark biological drifts against the classical biological-variation framework and assess their consistency with RCV thresholds across routine biomarkers. Methods: For 62 routine biomarkers, biological drift levels were compared with RCVs after transformation to test the consistency between the two frameworks. Results: Severe biological drifts mostly exceeded the 95% RCV threshold, indicating changes unlikely to be explained by short-term biological variation alone. In contrast, moderate drifts reached the 95% RCV threshold for approximately one in two biomarkers, suggesting that many moderate distance-to-optimum deviations may remain within expected variability, particularly for biomarkers with large within-subject variation CVi. Results are particularly interesting for the follow-up of people with diabetes and for the management of thyroid and hepatic disorders. Conclusions: Biological drifts derived from optimized personalized reference populations are broadly consistent with the RCV framework for identifying biologically meaningful deviations from the optimum and may therefore be relevant for the monitoring of certain biomarkers across several medical conditions in clinical practice.

Cell-free DNA (cfDNA), released from apoptotic and necrotic cells into body fluids, represents a non-invasive source of genetic information for disease prediction, diagnosis, and monitoring. However, its low physiological abundance makes cfDNA highly susceptible to pre-analytical influences. In particular, genomic DNA (gDNA) released from lysed white blood cells (WBCs) can contaminate plasma and compromise downstream cfDNA analyses. This study evaluated the impact of different blood collection tubes and isolation methods on cfDNA stability and yield. Blood samples from 13 healthy donors were collected using cfDNA-stabilizing tubes (Cell-Free DNA BCT, Streck; S-Monovette cfDNA Exact, Sarstedt) and stored at room temperature for 1, 5, or 10 days before plasma isolation. CfDNA was extracted using either a magnetic bead-based method or a silica column-based approach. DNA quantity and quality were assessed by fluorometric quantification, automated fragment analysis, and gene-specific quantitative PCR. Streck-based workflows maintained stable cfDNA yields and characteristic mononucleosomal fragmentation profiles across all storage times. In contrast, Sarstedt tubes showed reduced cfDNA concentrations after 5 days and a pronounced increase at 10 Days, accompanied by high-molecular weight DNA patterns consistent with WBC lysis. These trends were largely independent of the extraction method. Overall, the results demonstrate that blood collection tube chemistry critically influences cfDNA integrity during delayed processing. Streck tubes, particularly when combined with QIAamp, provided the most robust and reproducible workflow for routine molecular diagnostics, whereas Sarstedt tubes produced physiologically implausible results after extended storage.

Background: Prospective pregnancy registries and biobanking infrastructures are essential for future translational studies investigating maternal, placental and offspring health. However, circulating nucleic acid analyses are highly sensitive to preanalytical variability, particularly regarding blood-collection tube type and sample processing conditions. We established a prospective pregnancy registry and biobanking workflow at Cruces University Hospital and evaluated the impact of preanalytical variables on circulating cell-free DNA (cfDNA) and cell-free RNA (cfRNA) preservation in maternal plasma collected at delivery. Methods: The Registry of Pregnant Women at Cruces University Hospital was designed as a prospective infrastructure integrating placental sampling, maternal blood collection and ethically controlled future access to maternal and offspring clinical data. Within this framework, peripheral blood samples from 50 women at delivery were simultaneously collected into EDTA, Norgen and Roche tubes. Plasma samples processed within or after 24 hours following collection underwent cfDNA/cfRNA extraction, electrophoretic profiling, fluorometric quantification and RT-qPCR analyses targeting different stress-related genes. Results: By the end of June 2026, 1,127 women had been prospectively recruited into the registry, with 661 plasma samples, 637 serum samples and 858 sets of four placental biopsies collected, processed and stored in the Basque Biobank. In the preanalytical substudy, EDTA tubes yielded higher cfDNA concentrations, likely reflecting reduced cellular preservation and genomic DNA contamination. In contrast, Roche tubes showed superior cfRNA preservation, with higher cfRNA concentrations and more consistent detection of the characteristic 5S rRNA peak compared with EDTA and Norgen tubes. Processing delays beyond 24 hours reduced cfRNA concentration, while associations between circulating transcripts and gestational age were more consistently detectable in preservative-containing tubes. Conclusions: Prospective infrastructures like ours offer strong foundation for large scale, long-term studies in the framework of the Developmental Origins of Health and Disease hypothesis. Technically, Roche tubes provided superior cfRNA preservation and enhanced sensitivity for detecting subtle biological associations, supporting the importance of standardized preanalytical workflows within prospective pregnancy biobanking resource.

While cycle threshold (Ct) values from quantitative PCR (qPCR) serve as the gold-standard indicators of target abundance, their clinical interpretation is frequently confounded by inherent variability across diverse assay designs, reagents, and instrumentation. In this study, we present a data-driven consensus framework for Ct evaluation that uses large-scale, multi-assay amplification data to establish reference patterns of normal Ct behavior. Based on a total of 41,770 amplification curves collected from four routine diagnostic assays across two PCR platforms, we evaluated machine learning models across three experimental scenarios: within-platform validation, cross-assay generalization, and cross-platform transfer. Extreme gradient boosting (XGBoost) achieved the most accurate and stable predictions under data-sufficient, within-platform conditions with a mean absolute error (MAE) of 0.0419, while pooled multi-assay training improved cross-assay robustness compared with single-assay models. Model performance was further assessed using a deviation-based metric to quantify differences between predicted and instrument-reported Ct values, allowing efficient identification of anomalous amplification curves in large datasets. Notably, direct application across platforms without recalibration led to a substantial decline in performance, with a MAE of 2.62, showing platform-dependent variability. These findings indicate strong stability under within-platform and cross-assay conditions, with scalability contingent upon appropriate cross-platform calibration.

Background: The Prosigna Breast Risk of Recurrence test is based on the PAM50 classifier and was originally validated as an in vitro diagnostic (IVD) test on the Dx enabled nCounter(R) Analysis System. The Prosigna test is intended for early-stage, hormone receptor+ (HR+) breast cancer and provides the risk of recurrence (ROR) score (0-100), intrinsic subtype (Luminal A, Luminal B, HER2-enriched, and Basal-like), and the 10-year probability of distant recurrence. We describe the performance of the Prosigna test as a whole transcriptome RNA sequencing laboratory developed test (LDT) for measuring the Prosigna ROR score and intrinsic subtypes on tissue from surgical resection and core needle biopsy as compared to the Prosigna test on the nCounter system. Methods: We evaluated three separate breast cancer cohorts to 1) bridge the IVD test on the nCounter system and NGS LDT test (n = 245), 2) validate the bridged algorithm on an independent biobank sample set (n = 187), and 3) retrospectively test performance on long-term archival samples from a previous study (n = 109). Results: Bridging analysis showed minimal score variability and robust correlation of Prosigna ROR scoring in surgical resections (SR) (2.459, SD; 0.981, R2) and core needle biopsy (CNB) (2.338, SD; 0.970, R2) samples. In the validation set, the Prosigna NGS LDT ROR scores maintained high correlation to the scores of the nCounter system (SR = 0.968, CNB = 0.966, R2), exhibited minimal score variability (SR = 2.488, CNB = 2.558, SD), and demonstrated high concordance in subtype classifications (SR = 92.3% CNB = 92.8%). Further testing demonstrated comparable performance across tumor fractions, a lower limit of detection (LLOD) of 5 ng, and robustness to exogenous ethanol or genomic DNA contamination. When testing previously extracted RNA from the clinical cohort, we observed high correlation (0.974, R2) and low variance (3.078, SD) of ROR scores with original values on the nCounter system, along with strong risk group (95.4%) and subtype (94.5%) concordance. Conclusions: This study describes the analytical validation of the Prosigna NGS-based LDT measuring the Prosigna ROR score and intrinsic subtypes with robust analytical performance on SR and CNB specimens, providing confidence for clinicians utilizing the NGS-based version of this well-established test.

Cancer treatment has shifted toward personalized therapy based on molecular profiling, particularly in advanced disease. Existing circulating tumor DNA panels are often broad, generating many non-actionable variants and incurring costs that limit routine use in molecular tumor boards. We developed and validated a manufacturer-independent, 109-gene liquid biopsy-centered pan-cancer open next generation sequencing panel (LION panel), combined with an in-house bioinformatic pipeline to support clinical decision-making. A total of 87 samples were analyzed, including 17 reference samples, 21 healthy blood donor controls, and 49 patient samples including nine tumor entities. The LION panel achieved 92% sensitivity and 99% specificity in reference samples, with high concordance to digital droplet PCR (r = 0.99). It detected variant allele frequencies as low as 0.05% (tumor-informed) and 0.5% (tumor-uninformed). Clinical concordance reached 82% with blood-based digital droplet PCR and 75% with whole exome tissue sequencing. In representative cases, variant dynamics correlated with disease progression and revealed additional targetable variants. Overall, the LION panel supports clinical decision-making by enabling identification of targetable variants, disease monitoring, and detection of treatment resistance, particularly when tumor tissue is unavailable.

Melanoma incidence continues to rise globally, with formalin-fixed paraffin-embedded (FFPE) tissue archives representing an invaluable resource for large-scale retrospective proteomic studies. However, inconsistent deparaffinization remains a critical pre-analytical bottleneck limiting protein yield, reproducibility, and downstream data quality. In this study, we developed and validated a fully automated FFPE deparaffinization workflow using the Fluent(R) 780 liquid handling workstation (Tecan (C)) and evaluated its performance against a conventional manual protocol in a cohort of 54 patients with primary cutaneous melanoma, predominantly at early AJCC 8th edition stage I-II. The automated workflow achieved superior protein identification (6,146 {+/-} 860 vs. 4,941 {+/-} 1,091 proteins; p < 0.0001) with lower technical variability, while maintaining highly comparable global proteomic profiles as confirmed by principal component analysis and hierarchical clustering. A total of 8,305 proteins (96.1%) were identified by both methods, supporting the reproducibility and equivalence of the automated approach. Patients were stratified by the presence (N=21) or absence (N=33) of histological regression in the primary tumor. Proteomic comparison revealed 97 upregulated and 226 downregulated proteins in regressing melanomas, with pathway enrichment analysis demonstrating elevated mitochondrial and translational activity alongside reduced innate immune and complement pathway activation in the regression group. No statistically significant differences in overall, disease-free, or progression-free survival were observed between groups, consistent with the early-stage composition of the cohort. Digital pathology validated tissue morphology preservation across processing conditions. These findings support the integration of automated FFPE processing with proteomic and digital pathology workflows as a scalable platform for precision melanoma research. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/744404v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1d51629org.highwire.dtl.DTLVardef@a1f126org.highwire.dtl.DTLVardef@1df1b0aorg.highwire.dtl.DTLVardef@686f1c_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background: Plasma proteomics may identify host-response signatures in sepsis, but it is unclear whether extracellular vesicle (EV)-enriched plasma provides distinct or redundant information compared with plasma. We compared paired plasma and EV-enriched plasma proteomes in critically ill patients with sepsis and critically ill non-sepsis controls (CINS). Methods: In this prospective observational study, paired plasma and EV-enriched plasma samples were analyzed from 56 critically ill adults, including 40 patients with sepsis and 16 CINS patients. Protein abundance was quantified using liquid chromatography-tandem mass spectrometry. Analyses compared proteomic depth, protein overlap, global concordance between compartments, and differential protein abundance between CINS and sepsis. Exploratory Gene Ontology enrichment was performed as a supplementary analysis. Results: EV-enriched plasma expanded proteomic detection, identifying 2,476 filtered proteins compared with 506 in plasma. Only 386 proteins were detected in both compartments, while 2,090 were unique to EV-enriched plasma and 120 were unique to plasma. Among shared proteins, plasma and EV-enriched plasma showed modest global concordance across critically ill patients (Spearman coeff = 0.322, p = 9.19 x 10^-11), with similar findings in sepsis alone. Differential abundance analysis identified 11 sepsis-associated proteins in plasma and 22 in EV-enriched plasma. Only SAA1, SAA2, and IGFBP6 were significant in both compartments. Exploratory pathway analysis supported acute-phase and inflammatory enrichment in plasma sepsis-associated proteins, while EV-enriched signals were directionally plausible but did not meet prespecified FDR thresholds. Conclusion: Plasma and EV-enriched plasma proteomics capture related but nonredundant sepsis-associated host-response information in critically ill patients.

Objectives: Capturing adequate blood volume for blood cultures is critical for accurate detection of bloodstream infections. Pediatric volume targets vary by age and weight, whereas adult targets are standardized. The BD BACTEC FXI Culture System (FXI) contains an integrated calibrated load cell capable of automatically reporting blood volume measurements for each vial loaded onto the system. This study evaluated the accuracy of the FXI's blood volume measurements in simulated pediatric and adult patients. Methods: Mock pediatric and adult blood draws were performed, using bagged whole blood, to replicate real-world collection protocols. Syringe-collected blood volumes ranged from 2.0 to 15.0 mL for pediatric patients, depending on mock patient weight, and were fixed at 40.0 mL for adults. Samples were inoculated into BD BACTEC Peds Plus/F, Plus Aerobic/F, and Lytic/10 Anaerobic/F Culture Vials, with a target volume of 2.0 to 10.0 mL per bottle. Reference blood volumes were determined gravimetrically using manually obtained pre- and post-inoculation weights with a blood-specific gravity of 1.055 g/mL and were compared to the automatically measured, gravimetric-based blood volumes reported by the BACTEC FXI Culture System. Results: Automated volume estimates were accurate to a mean error of -0.03 mL per bottle (SD, 0.40 mL; n=168; 95% CI, -0.09 mL, 0.03 mL) and -0.08 mL (SD, 0.79 mL; n=72; 95% CI, -0.26 mL, 0.10 mL) when assessing total volume collected per patient. Conclusions: Our findings demonstrate that the automated system can quantify blood volumes in BACTEC culture vials and support blood volume monitoring for pediatric and adult collections. The gravimetric approach is also amenable to full automation for efficient and accurate blood volume determination.

Equitable access to large sample cohorts for robust, high-throughput proteomics for biomarker discovery is a major barrier to widescale clinical implementation. Dried blood spots (DBS) offer a minimally invasive alternative to venous blood draws, enabling at-home microsampling (<50 {micro}L) for centralised analysis, thus enhancing research participation. This approach is particularly relevant for under-represented groups, including children, the elderly, minority backgrounds and those with long-term health conditions such as chronic kidney disease (CKD), where disease fluctuations may occur outside the clinic, and vein preservation is critical. Proteomic analysis has demonstrated great utility in monitoring disease progression, and for biomarker/therapeutic target discovery. However, liquid chromatography-tandem mass spectrometry (LC-MS/MS) of whole blood is hindered by the wide dynamic range and the relatively high abundance of proteins such as haemoglobin, compromising biomarker discovery. Here, we establish an optimised workflow for protein extraction and haemoglobin depletion from microsamples obtained using DBS, enabling sensitive and high-throughput proteomic analysis. We demonstrate that haemoglobin depletion increases protein identifications by [~]50%, mitigating ion suppression and dynamic range effects, enabling the identification of putative biomarkers from patients with stage 5 CKD on dialysis. We also evaluated a commercial cell-free DBS device which yielded a sample more representative of plasma compared to traditional DBS and enabled greater depletion of haemoglobin compared to traditional DBS with haemoglobin depletion methods. Our findings offer a scalable approach for biomarker discovery, facilitating remote, longitudinal clinical studies.

Accurate discrimination between viral, bacterial, and inflammatory diseases in febrile children remains a major clinical challenge that contributes to diagnostic uncertainty, inappropriate antimicrobial use, and suboptimal clinical management. Host blood transcriptomics offer a promising strategy to improve diagnostic precision. The present study represents the largest integrative multi-cohort pediatric study of transcriptomic biomarker discovery, validation, and confirmation reported to date, integrating harmonized public transcriptomic datasets with an independent confirmation cohort comprising well-phenotyped patients to identify parsimonious host-response signatures for differentiating viral, bacterial, and inflammatory diseases. Transcriptomic signatures were derived from an integrated retrospective microarray multi-cohort (n=1,683), independently validated in a retrospective RNA-seq cohort (n=767), and confirmed by digital PCR in an independent cohort (n=29), demonstrating reproducibility across patient populations, transcriptomic technologies, and analytical platforms. The analysis identified binary signatures and a unified multiclass classifier that consistently achieved high diagnostic accuracy across all three study phases and outperformed more than 30 published host transcriptomic signatures. Decision curve analysis showed substantially greater clinical net benefit than C-reactive protein across clinically relevant decision thresholds. These findings provide a strong foundation for clinically deployable molecular diagnostics to improve patient triage, antimicrobial stewardship, and precision medicine in childhood infections.

Introduction: Congenital cytomegalovirus (cCMV) is the most common congenital viral infection and a leading non-genetic cause of neurodevelopmental impairment. Current diagnostic methods using fetal biofluids provide limited insight into fetal pathophysiology. Extracellular vesicles (EVs) in amniotic fluid (AF) are a promising source of stable biomolecules that reflect real-time fetal physiology. This proof-of-concept study compared amniotic fluid EV (AF-EV) characteristics in fetuses with severe CMV infection with those of uninfected fetuses and aimed to develop hypotheses about fetal response to cCMV in utero. Methods: AF samples were collected from pregnancies with symptomatic CMV infection and gestational-age-matched uninfected controls (4 pairs, n=8 total). EVs were isolated and characterised by Western blotting, cryo-electron microscopy, and nanoparticle tracking analysis. Label-free quantitative proteomics identified CMV-associated changes in the AF-EV proteome. Results: CMV-infected AF showed higher vesicle levels (Hedges' g = 1.55), indicating inflammation and virus-induced changes in EV biogenesis. Proteomic analysis found 8.6% of proteins dysregulated. Upregulated proteins included haemoglobin subunits, immunoglobulin heavy chain mu, and myeloperoxidase (Hedges' g = 1.51 to 1.88), indicating haemolysis and immune activation. Eleven host proteins related to neurodevelopment, mitochondrial function, lipid metabolism, and Golgi trafficking were absent in infected cases, indicating viral disruption of host pathways. Protein enrichment analysis revealed differences in neurological, haematological, and immune pathways, aligning with severe cCMV pathology. Conclusion: This study acts as a proof-of-principle investigation of the AF-EV proteome in cCMV. Although the results highlight key protein signatures associated with severe fetal outcomes, they primarily serve to generate hypotheses and inform larger prospective studies.

The reproducibility of immunohistochemistry in tumor tissue analysis across reference labs remains a persistent challenge. We tested the extent to which an intra-slide calibration technology mitigated discprepencies in inter-laboratory assays of p53 immunohistochemical (IHC) reactions in brain biopsies of glioblastoma (GB), IDH-wildtype. Intra-slide calibration technologies apply a 0-100% concentration scale incorporating primary surrogate and secondary antibodies to generate a standardized curve for DAB precipitation. IHC from GB samples was performed independently by pathology departments from two different hospital laboratories and were digitalized at 40x magnification using Aperio Image Scope software. Feature extraction, including intensity and texture parameters was performed using the EBImage package in R, followed by UMAP dimensionality reduction and DBSCAN clustering analysis. Our results show significant differences in intensity and texture clustering patterns between laboratory tissue samples and intra-slide calibration technology ruler caused by the different laboratories. Intra-slide calibration technology coupled with polynomial regression analysis improved ~90% the data harmonization. Our findings demonstrate a key role for computational pathology using intra-slide calibration technology to enable intra-laboratory consistency and inter-laboratory reproducibility. These advances strengthen the reproducibility of diagnostic assessments and support more objective, data-driven decision-making in neuro-oncology.

The isolation of cancer cells and rare circulating tumour cells (CTCs) and the initiation of patient-derived organoids (PDOs) represent two critical new approach methodologies (NAMs) for advancing precision oncology and drug discovery. However, current technologies encounter significant limitations, including high system pressures that compromise cell viability, sample loss, and reliance on marker-dependent enrichment strategies. Here, we performed a technical validation of the Pala Single Cell Sorter/Dispenser (Bio-Techne) using cancer cell lines spiked into healthy donor blood as a model for CTCs, alongside cells isolated from ovarian cancer (OC) patients and cervical cancer cell lines. The platform achieved up to 80% single-cell dispensing efficiency under gentle sorting conditions (<2 psi), successfully dispensing single cancer cells, cell clusters, and cancer cells spiked into blood (mimicking CTCs). Concurrently, three-dimensional organoid structures generated from dissociated OC samples and cervical cancer cell lines showed viable growth and cluster formation within one week. Compared to literature values for fluorescence-activated cell sorting (FACS), the Pala maintained higher post-sort viability (88% vs. 55-70%) and organoid initiation efficiency (68% vs. 42%). This work establishes the Pala as a flexible tool for patient cancer cell dispensing, CTC-mimic isolation, and PDO generation within drug discovery workflows. Clinical validation using authentic patient CTCs remains necessary prior to clinical implementation.

Although the impact of delayed processing on gene expression in EDTA blood has been well documented using targeted assays and microarray platforms, the emergence of next-generation RNA sequencing (RNA-seq) has not yet been leveraged to systematically compare these effects against stabilized whole blood collection systems. Notably, no study has performed a time course RNA-seq analysis with human bulk RNA of matched EDTA and PAXgene blood RNA samples drawn from the same subjects. EDTA is still widely used for gene expression analysis studies. Yet, the genome-wide dynamics by which EDTA blood transcriptomes deviate from a stabilized reference over time remain poorly defined. This represents an important methodological gap, given the increasing reliance on RNA-seq for biomarker discovery, clinical transcriptomics and diagnostics.

Background and Objective: Due to the heterogeneity of bladder cancer, minimally invasive molecular profiling may improve tumor characterization at the time of diagnosis. We evaluated whether integrated genomic and fragmentomic profiling of plasma and urinary circulating tumor DNA (ctDNA) detects BC-derived signals for diagnosis and disease stratification across all tumor stages. Methods: In this real-world cohort, 202 plasma and urine samples were obtained from 33 patients with non-muscle-invasive BC (NMIBC), mostly Ta tumors, and 15 patients with muscle-invasive BC (MIBC), as well as from 58 cancer-free controls. Low-coverage whole-genome sequencing was performed to assess ctDNA fragmentation, chromosomal instability and copy number variations. Matched tumor tissue was analyzed to evaluate concordance between liquid biopsy and tissue-derived molecular alterations. Key Findings and Limitations: Complementary genomic and fragmentomic profiling of cfDNA achieved detection rates of 75.8% in NMIBC patients and 91.7% in MIBC patients with paired plasma and urine. Distinct differences were observed between MIBC, NMIBC and cancer-free controls, consistent with increasing ctDNA signals during disease progression. Tumor tissue analysis confirmed BC-associated molecular alterations. Limitations include the single-center design and limited sample size. Conclusions and Clinical Implications: Multimodal profiling of plasma and urinary cfDNA enabled the detection of tumor-derived molecular signals for all bladder cancer stages, including early-stage disease. By integrating genomic and fragmentomic features, this minimally invasive approach provides molecular tumor characterization at the time of diagnosis and may support future risk-adapted diagnostic, therapeutic and surveillance strategies.

BACKGROUND In line with International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) recommendations for high-sensitivity cardiac troponin assays, analytical validation and reference limit assessments are required to confirm that an assay meets performance criteria. This study evaluated the analytical performance and established the 99th percentile upper reference limit (URL) for the SPINCHIP High-Sensitivity Cardiac Troponin I (SPINCHIP hs-cTnI) point-of-care assay. METHODS Analytical performance characteristics, including the limit of blank (LoB), limit of detection (LoD), and limit of quantification (LoQ), were assessed. Additionally, 1,053 plasma samples and 1,055 whole-blood samples were used to determine the URL. Imprecision around the 99th percentile URL was evaluated as part of the analytical validation. High-sensitivity criteria were assessed by confirming measurable cTnI in [&ge;]50% of healthy individuals (n=432 plasma; n=431 whole blood) and achieving imprecision <10% at the 99th percentile (plasma, n=960; whole blood, n=480). RESULTS SPINCHIP hs-cTnI demonstrated a LoB of 0.3 ng/L; LoDs of 0.8 ng/L (plasma) and 0.9 ng/L (whole blood); and LoQs of 1.1 ng/L (plasma) and 1.4 ng/L (whole blood). The analytical measuring range was 1.1-9,000 ng/L. Imprecision at the common 99th percentile URL (14 ng/L) was 5.8%; for men (URL=16 ng/L) 5.6% and for women (URL=10 ng/L) 6.3%. Greater than 85.2% (94.0% and 76.1% in men and women, respectively) of healthy individuals showed measurable cTnI above the LoD. CONCLUSIONS The SPINCHIP hs-cTnI assay meets the IFCC high-sensitivity requirements, demonstrating <10% imprecision at the 99th percentile, reliable low-concentration precision and cTnI detection in more than half of healthy individuals.

BackgroundTraditional immunohistochemistry (IHC) with chromogen detection has limited multiplex capacity, detecting at most 4 protein markers per tissue section simultaneously, thereby restricting comprehensive spatial analysis of valuable human biopsies. We developed and validated a robust serial IHC (sIHC) staining method to detect multiple antigens on a single kidney biopsy slide, maximizing data yield for diagnosing and studying complex kidney diseases. MethodsFormalin-fixed, paraffin-embedded kidney biopsy sections were subjected to repeated IHC/imaging cycles with antibody removal using an optimized sodium dodecyl sulfate-glycerol buffer stripping protocol. Images were then co-registered, and analysis was performed using a variety of methodologies, including color deconvolution, cell segmentation, and spatial clustering. ResultsThis optimized sIHC method successfully detected up to 20 antigens on a single slide. Combining image analysis and artificial intelligence software, for example with HALO (Indica Labs), the assay assembles high-dimensional images and enables quantitative histology and single-cell spatial analysis. Using this advanced method, we were able to identify rare cell populations, such as double-negative T cells, that are challenging to detect conventionally. ConclusionWe have developed a validated, high-capacity sIHC protocol that uses standard IHC procedures with commercially available, clinically validated off-the-shelf antibodies. This method is a valuable, cost-effective tool for obtaining extensive, high-dimensional single-cell-resolved spatial data from limited pathology samples, such as a human kidney biopsy.

An important step toward clinical implementation of (epi-)genomic assays on liquid biopsies is their validation on identical samples within and across laboratories. For these validation studies, there is a need for cell-free DNA (cfDNA) samples with defined tumor fractions and (epi-)genomic aberrations. However, the amount of circulating cfDNA isolated from patient samples is often limited, especially in pediatric cases. Additionally, patient samples contain a high degree of variability in cfDNA yield and tumor fraction. Several commercial artificial cfDNA products are available for validation studies, however their use is restricted to specific assays, aberrations and/or tumor entities. Alternatively, artificial cfDNA samples can be produced by fragmenting genomic DNA to mimic highly fragmented cfDNA derived from both tumor and healthy blood, followed by mixing artificial tumoral and healthy cfDNA at defined fractions. In this study, we compared native cfDNA with artificial cfDNA generated by three different fragmentation methods, including sonication and two enzymatic digestions using micrococcal nuclease and double-stranded deoxyribonuclease (dsDNase). We assessed fragment length profiles, end motifs and nucleosome occupancy patterns from shallow whole-genome sequencing data, as well as coverage profiles from targeted panel sequencing, together with a small-scale mixing experiment of tumor and healthy cell derived artificial cfDNA. Although sonication remains a convenient high-throughput approach to generate artificial cfDNA for certain downstream applications, enzymatic fragmentation, particularly the dsDNase-based method, more faithfully reproduced native cfDNA characteristics.