Clinical Chemistry

BackgroundCirculating tumor DNA (ctDNA) is a non-invasive biomarker that can be used as a tool to detect minimal residual disease (MRD). MRD can provide important prognostic information in diffuse large B-cell lymphomas (DLBCL). Here, we present an MRD assay with an improved detection method for ctDNA, Phased Variant Enrichment and Detection Sequencing (PhasED-Seq) which leverages phased variants (PVs) to detect ctDNA. MethodsPlasma samples from non-cancer controls were used to assess assay specificity. A limiting dilution series using DLBCL clinical-contrived samples was performed to assess assay sensitivity and precision. The accuracy of the PhasED-Seq-based assay was assessed using plasma samples from individuals with DLBCL and for whom MRD comparator assay results were also available. All samples were sourced from commercial vendors or academic studies. ResultsThe analytical and clinical performance of the MRD assay was evaluated using clinical and clinical-contrived DLBCL samples. The assays false positive rate was 0.24% and the background error rate was 1.95E-08. The limit of detection at 95% detection rate (LoD95) at 120 ng was 0.7 parts in 1,000,000 and precision was >96%. Clinical accuracy was 90.62% PPA and 77.78% NPA. ConclusionsThe PhasED-Seq-based MRD assay has strong analytical and clinical performance in B-cell dyscrasias. Through the development of improved ctDNA detection methods such as that presented here, patient outcomes may be improved through the detection of residual disease or early relapse which may be used to guide treatment decisions. Brief SummaryHere we present the analytical validation of a non-invasive minimal residual disease (MRD) assay which uses Phased Variant Enrichment and Detection Sequencing (PhasED-Seq) to improve the error profile and sensitivity of circulating tumor DNA (ctDNA) detection. The assays performance included a false positive rate of 0.24% and a background error rate of 1.95E-08. The limit of detection at 120 ng was 0.7 parts in 1,000,000 (6.61E-07 PVAF) with precision >96%. Positive and negative agreement were 90.62% and 77.78%, respectively. This suggests that the PhasED-Seq-based MRD assay is accurate and reproducible, thus appropriate for clinical use for individuals with B-cell malignancies.

BackgroundPlasma-separating dried blood spots (psDBS) allow measurement of cancer biomarkers such as cell-free DNA concentration, enabling distributed sample collection, as well as ambient storage and shipment. However, unlike volumetric blood tube collections, blood volume collected using psDBS can vary between individuals. We evaluated whether analysis of psDBS mass, area, and color can be used to infer blood volume, and enable downstream measurement of circulating analyte concentrations. MethodsWhole blood from healthy individuals and cancer patients was collected and used to prepare psDBS with volumes of 50 to 250 {micro}L. Total area and mean grey value (MGV) of the erythrocyte region were measured using image segmentation. We developed a linear regression model to predict whole blood volume using mass, area, and MGV. DNA from psDBS was extracted, then quantified using a quantitative PCR assay targeting L1PA2. ResultsWe analyzed 261 psDBS samples from 43 healthy individuals and 118 cancer patients. Using 110 samples from 10 healthy individuals, the linear regression model showed a strong correlation between actual and predicted blood volume (Pearson r = 0.97, RMSE = 5.1 {micro}L). Using 151 samples from 33 separate healthy individuals and 118 samples from cancer patients, the model predicted blood volume with high accuracy (RMSE = 15.1 {micro}L and 13.3 {micro}L, respectively). Plasma DNA concentration in psDBS was moderately correlated with matched blood tubes (Spearman rho = 0.62). ConclusionOur results show estimating blood volume collected on psDBS is feasible using mass and image analysis. This approach enables calculation of circulating concentration for plasma analytes measured using psDBS.

IntroductionDespite its promise, accuracy of microbial cell-free DNA (mDNA) in plasma as a diagnostic tool is hindered by its low abundance and process contaminants. We have previously shown that combining size selection with single-stranded DNA (ssDNA) library preparation increased mDNA yield by 200-fold but also decreased sensitivity for pathogen detection due to higher background noise. A recent study showed that pathogen-derived DNA was enriched for CC dinucleotide at 5 ends compared to contaminants. Since ssDNA libraries preserve sequence motifs at both ends (5 and 3), we hypothesized that analysis of nucleotide motifs at microbial fragment ends in size-selected ssDNA libraries could help differentiate pathogen DNA from background noise. MethodsWe performed deep sequencing on size-selected ssDNA libraries (<110 bp) generated from longitudinal plasma samples of 11 critically-ill patients (5 with culture-proven infections, 20 samples; 6 without infections, 18 samples) and 6 no-template controls (NTCs). For each 2-mer and 1-mer motif, we calculated the ratio between its frequency observed at 5 and 3 fragment ends in sequencing data and its expected frequency in the corresponding reference genome (O/E ratio). We compared enrichment of motifs in pathogen DNA and contaminant DNA fragments. ResultsPathogen-derived mDNA fragments were more biased in O/E end motif ratios compared to contaminants across all 3 groups (NTCs, no-infections and culture-proven infections), at both 5 and 3 fragment ends. Notably, the GG dinucleotide was enriched at the 3 end in pathogens compared to contaminants (P < 0.0001). Combining O/E ratios for C and G nucleotides at the 3 end achieved areas under the receiver operating characteristic curve of >0.98 for distinguishing common contaminants from culture-proven pathogens. ConclusionsPathogen-derived mDNA in size-selected ssDNA libraries is biased at 5 and 3 fragment end compared to contaminants. Incorporating microbial fragment end motif analysis can enhance signal-to-noise ratio and improve pathogen detection and identification in plasma metagenomic sequencing.

Human proteases play major roles in various pathological conditions, including dysregulated immune responses in sepsis, making them strong candidates for developing diagnostic markers. Despite this potential, the progress of developing protease-based diagnostic tools has remained slow due to significant technical barriers associated with measuring protease activity, mainly stemming from the vast diversity and the lack of substrate specificity, which complicate the interpretation of protease activity profiles. In this work, we advanced the current state of assay development by designing substrate molecule sensors and implementing an analytical approach based on mass spectrometry. Specifically, we chemically modified protease substrates for human neutrophil elastase (HNE) and matrix metalloproteinases (MMPs) to enhance specificity in mass spectrometry. This approach yields distinct cleavage products with non-overlapping mass-to-charge signatures, allowing precise differentiation of each proteases activity. We then integrated the modified substrates into a mass spectrometry-based multiplexed assay platform, enabling quantification of multiple protease activities in a single run. We applied the assay to plasma samples and demonstrated that the assay detects distinct protease activity profiles. Our study demonstrated that the assay achieved a diagnostic sensitivity of 88% and specificity of 87% for sepsis detection. The combination of low cost, rapidness, and robust diagnostic performance makes this platform well-suited to a wide range of clinical settings. One Sentence SummaryNovel modifications to protease substrates enable a multiplexed activity assay for accurate sepsis diagnosis in a 3-hour timeframe.

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.

BackgroundCurrent next generation sequencing (NGS) and microarray based Non-Invasive Prenatal Tests (NIPT), used for the detection of common fetal trisomies, are still expensive, time consuming and need to be performed in centralized laboratories. To improve NIPT in clinical routine practice as universal prenatal screening, we have developed a digital droplet PCR (ddPCR) based assay called iSAFE NIPT using cell free fetal DNA (cffDNA) for detection of fetal trisomies 13, 18 and 21 in a single reaction with advantage of high diagnostic accuracy and reduced cost. Materials and MethodsWe first used artificial DNA samples to evaluate analytical sensitivity and specificity of the iSAFE NIPT. Next, we analysed 269 plasma samples for the clinical validation of iSAFE NIPT. Fifty-eight of these, including five trisomies 21, two trisomies 18 and one trisomy 13 were utilised to establish the assay cut-off values based on ratios between chromosome counts. The remaining 211 plasma samples, including 10 trisomies 21, were analysed to evaluate iSAFE NIPT clinical performance. ResultsiSAFE NIPT achieved a 100% analytical sensitivity (95% CI 94.9-100% trisomy 21; 79.4-100% trisomy 18; 73.5-100% trisomy 13) and 100% specificity (95% CI 96.3-100% trisomy 21; 97.6-100% trisomy 18; 97.6-100% trisomy 13). It also achieved a 100% clinical sensitivity and specificity for trisomy 21 detection in the 211 clinical samples (95% CI for sensitivity is 69.1-100%, and 95% CI for specificity is 98.2-100%). ConclusionsThe iSAFE NIPT is a highly multiplexed ddPCR based assay for detection of fetal trisomies from maternal blood. Based on clinical validation, the iSAFE NIPT has high diagnostic sensitivity and specificity. It can be decentralized in routine clinical laboratories, is fast, easy to use and economical comparing to current NIPT.

Sepsis is the bodys dysfunctional response to infection associated with organ failure. Delays in diagnosis have a substantial impact on survival. Herein, samples from 586 in-house patients were used in conjunction with machine learning and cross-validation to narrow a gene expression signature of immune cell reprogramming to predict clinical deterioration in patients with suspected sepsis within the first 24 hours (h) of clinical presentation using just six genes (Sepset). The accuracy of the test ([~]90% in early intensive care unit (ICU) and 70% in emergency room patients) was validated in 3,178 patients from existing independent cohorts. A real-time reverse transcriptase polymerase chain reaction (RT-PCR)-based test was shown to have a 98% sensitivity in >230 patients to predict worsening of the sequential organ failure scores or admission to the ICU within the first 24 h following Sepset detection. A stand-alone centrifugal microfluidic instrument that integrates the entire automated workflow for detection of the Sepset classifier in whole blood using digital droplet PCR was developed and tested. This PREcision meDIcine for CriTical care (PREDICT) system had a high sensitivity of 92%, specificity of 89%, and an overall accuracy of 88% in identifying the risk of imminent clinical deterioration in patients with suspected sepsis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC="FIGDIR/small/24314844v2_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@82f577org.highwire.dtl.DTLVardef@1c18921org.highwire.dtl.DTLVardef@111f119org.highwire.dtl.DTLVardef@ebbb87_HPS_FORMAT_FIGEXP M_FIG Description of Graphic AbstractFeature reduction and development of a gene classifier that predicts deterioration-risk-groups in patients starts with in-house RNA sequencing data from patient collected from a heterogenous cohort of patients with suspected sepsis (top left) to reduce our original published gene signature down to 6-genes (Sepset), for which expression could be related to 2 housekeeping genes. Feature selection was performed using machine learning (ML) and AI and the classifier validated in samples from published transcriptomic studies. Molecular assay is then developed by designing and testing primer/probe sequences specific to the target genes using digital droplet PCR. In parallel, sample-to-answer microfluidic platform and cartridges are developed (bottom right) and analytical performance of multiplex quantitative assay is tested. Prognostic enrichment is obtained by analyzing the results using ML algorithm to determine the percent likelihood of significant clinical deterioration within the immediate next 24 h. The deployment of PREDICT platform (center) at the point-of-care is anticipated to aid in triage and management of prospective sepsis within the first 3 h of clinical presentation. C_FIG

Lower Respiratory Tract Infections (LRTIs) represent the leading cause of death due to infectious diseases. Current diagnostic modalities primarily depend on clinical symptoms and lack specificity, especially in light of common colonization without overt infection. To address this, we developed a noninvasive diagnostic approach that employs BreathBiomics, an advanced human breath sampling system, to detect protease activities induced by bacterial infection in the lower respiratory tract. Specifically, we engineered a high-sensitivity and high-specificity molecular sensor for human neutrophil elastase (HNE). The sensor undergoes cleavage in the presence of HNE, an event that is subsequently detected via Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS). Application of this methodology to clinical samples, breath specimens collected from intubated patients with LRTIs, demonstrated the detection of the cleaved sensor by MALDI-TOF MS. Our findings indicate that this novel approach offers a noninvasive and specific diagnostic strategy for people with LRTIs. O_TEXTBOXSignificance The potential for using human breath for noninvasive disease detection and diagnosis has long been recognized, yet the lack of effective biomolecular sampling technologies has hindered progress. To address this limitation, we developed BreathBiomics, an advanced sampling system designed to efficiently capture biomolecules in human exhaled breath. By focusing on protease dysregulation, an established event induced by bacterial infections, we demonstrated that BreathBiomics can capture proteases and facilitate their subsequent activity-based detection for the diagnosis of LRTI. We verified the assays sensitivity and clinical applicability through empirical studies. Our work marks a significant advancement by providing the first viable pathway for the development of in vitro diagnostic assays leveraging human breath for disease detection and diagnosis. C_TEXTBOX

BackgroundsNext-generation sequencing (NGS) and droplet digital PCR (ddPCR) are both established methods for detecting EGFR mutations in non-small cell lung cancer (NSCLC). However, comprehensive validation of their concordance in mutation detection and variant allele frequency (VAF) quantification across heterogeneous sample types remains limited. Inconsistent results from different sample types (e.g., cfDNA, FFPE) pose a significant challenge to clinical decisions. This is particularly critical for advanced NSCLC patients, who often rely on liquid biopsy, yet the concordance between liquid and tissue-based testing lacks validation in large-scale studies. Another issue in clinical practice is that many tumor samples are limited in quantity, making it difficult to meet testing requirements. Therefore, it is worth exploring whether pre-capture NGS libraries can serve as substitutes for original DNA. MethodsIn this study, we first developed three assays to detect EGFR L858R, exon 19 deletions (Ex19del) and T790M, respectively using ddPCR platform. Their Limit of Detection (LOD) could reach 0.01% at 100 ng of input DNA. Subsequently, we conducted a large retrospective clinical study to systematically compare the detection performance of ddPCR and NGS across three mutation types using approximately 1,000 EGFR-positive samples, including cell-free DNA (cfDNA), pre-capture NGS libraries of cfDNA (cfDNA-prePCR), FFPE-derived DNA (ffpeDNA), pre-capture NGS libraries of FFPE-derived DNA (ffpeDNA-prePCR), fresh tumor tissue DNA (ttDNA), pre-capture NGS libraries of ttDNA (ttDNA-prePCR), pleural effusion supernatants DNA (peDNA), and pre-capture NGS libraries of peDNA (peDNA-prePCR). They were analyzed for detection concordance and VAF correlation. Especially, we made comparisons of the tumor DNA, including ctDNA and tumor tissue DNA, with their paired pre-capture NGS library. ResultsKey findings demonstrated excellent overall agreement between NGS and ddPCR. The mutation detection concordance rates were 98.72% (overall), with subtype-specific rates of 98.93% (L858R), 99.23% (Ex19del), and 97.14% (T790M). VAF measurements between ddPCR and NGS showed exceptional correlation (Pearsons r = 0.975, P<0.001). Notably, pre-capture NGS libraries showed remarkable VAF concordance with their source materials (0.993 for cfDNA libraries vs cfDNA; 0.998 for tumor tissue libraries vs tumor DNA with EGFR L858R; 0.991 for tumor tissue libraries vs tumor DNA with EGFR Ex19del). ConclusionsNGS and ddPCR demonstrate high concordance in EGFR mutation detection and VAF quantification, supporting their complementary roles in clinical testing. Using pre-capture libraries as an alternative to source samples can avoid repeat biopsies and enables subsequent testing for patients with inadequate FFPE sample quantity. These findings establish an evidence base for integrated diagnostic paradigms leveraging NGSs multiplexing power and ddPCRs sensitivity.

ObjectiveTo explore capillary blood collection as a potential method to enable more equitable access to non-invasive prenatal screening (NIPS) for aneuploidy. MethodsAll participants contributed venous and capillary blood samples in this comparative study. Samples were analyzed using standard NIPS methods. Venous samples were used as the basis. Multiple parameters were compared including specimen collection, cfDNA characteristics, and NIPS outcome. ResultsVenous and capillary sample pairs were successfully collected for comparative analysis from 202 participants. Cell free DNA (cfDNA) size profiles from venous vs capillary blood were not different (p >0.05). Evaluation of fetoplacental cfDNA contribution in plasma revealed no statistically significant difference in venous vs capillary samples. Elevated Z-scores for trisomy 21 (n=13), trisomy 18 (n=2), or trisomy 13 (n=1) were concordant between venous and capillary samples in all 16 pairs. ConclusionWe have demonstrated that sufficient capillary blood volumes can be obtained for NIPS. Furthermore, capillary and venous cfDNA samples have comparable characteristics. As NIPS results from capillary blood collections are equivalent to NIPS results obtained from venous blood, capillary blood collections are a potential candidate for a distributable NIPS system which promotes equitable access to NIPS for all pregnant patients.

BackgroundRegulatory approval of new over-the-counter tests for infectious agents such as SARS-CoV-2 has historically required that clinical trials include diverse groups of specific patient populations, making the approval process slow and expensive. Showing that populations do not differ in their viral loads--the key factor determining test performance--could expedite the evaluation of new tests. Methods46,726 RT-qPCR-positive SARS-CoV-2 viral loads were annotated with patient demographics and health status. Real-world performance of two commercially available antigen tests was evaluated over a wide range of viral loads. An open-access web portal was created allowing comparisons of viral-load distributions across patient groups and application of antigen-test performance characteristics to patient distributions to predict antigen-test performance on these groups. FindingsIn several cases distributions were surprisingly similar where a difference was expected (e.g. smokers vs. non-smokers); in other cases there was a difference that was the opposite direction from expectations (e.g. higher in patients who identified as White vs. Black). Sensitivity and specificity of antigen tests for detecting contagiousness were similar across most groups. The portal is at https://arnaoutlab.org/coviral/. ConclusionsIn silico analyses of large-scale, real-world clinical data repositories can serve as a timely evidence-based proxy for dedicated trials of antigen tests for specific populations. Free availability of richly annotated data facilitates large-scale hypothesis generation and testing. FundingFunded by the Reagan-Udall Foundation for the FDA (RA and JEK) and via a Novel Therapeutics Delivery Grant from the Massachusetts Life Sciences Center (JEK).

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.

The COVID-19 pandemic is challenging the global supply chain and equipment needed for mass testing with RT-qPCR, the gold standard for SARS-CoV-2 diagnosis. Here, we propose the RT-LAMP assay as an additional strategy for rapid virus diagnosis. However, its validation as a diagnostic method remains uncertain. In this work, we validated the RT-LAMP assay in 1,266 nasopharyngeal swab samples with confirmed diagnosis by CDC 2019-nCoV RT-qPCR. Our cohort was divided, the first (n=984) was used to evaluate two sets of oligonucleotides (S1 and S3) and the second (n=281) to determine whether RT-LAMP could detect samples with several types of variants. This assay can identify positive samples by color change or fluorescence within 40 minutes and shows high concordance with RT-qPCR in samples with CT [&le;]35. Also, S1 and S3 are able to detect SARS-CoV-2 with a sensitivity of 68.4% and 65.8%, and a specificity of 98.9% and 97.1%, respectively. Furthermore, RT-LAMP assay identified 279 sequenced samples as positive (99.3% sensitivity) corresponding to the Alpha, Beta, Gamma, Delta, Epsilon, Iota, Kappa, Lambda, Mu and Omicron variants. In conclusion, RT-LAMP is able to identify SARS-CoV-2 with good sensitivity and excellent specificity, including all VOC, VOI, VUM and FMV variants.

High throughput DNA sequencing technologies have undergone tremendous development over the past decade. Although optical detection-based sequencing has constituted the majority of data output, it requires a large capital investment and aggregation of samples to achieve optimal cost per sample. We have developed a novel electronic detection-based platform capable of accurately detecting single base incorporations. The GenapSys technology with its electronic detection modality allows the system to be compact, accessible, and affordable. We demonstrate the performance of the system by sequencing several different microbial genomes with varying GC content. The platform is capable of generating up to 2 Gb of high-quality nucleic acid sequence in a single run. We routinely generate sequence data that exceeds 99% raw accuracy with read lengths of up to 175 bp. Average quality scores remain above Q30 (99.9% raw sequencing accuracy) beyond 150 bp, with more than 85% of total bases at or above Q30. The utility of the platform is highlighted by targeted sequencing of the human genome. We show high concordance of SNP detection on the human NA12878 HapMap cell line with data generated on the Illumina sequencing platform. In addition, we sequenced a targeted panel of cancer-associated genes in a well characterized reference standard. With multiple library preparation approaches on this sample, we were able to identify low frequency mutations at expected allele frequencies.

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.

Blood biomarkers are central to monitoring disease progression and evaluating treatment responses, yet traditional venipuncture captures a single physiological snapshot in time and becomes burdensome with repeated sampling. Remote blood self-sampling offers a path toward longitudinal, decentralized monitoring, but maintaining protein integrity from draw to analysis remains a critical challenge. Here, we optimized pre-analytical blood collection and stabilization parameters to maintain protein levels at the time of collection for use with remote sampling technology. First, we optimized blood collection time with Tasso remote self-sampling devices to minimize interference from clotting, finding that a 2.5 min collection time best reduces clot formation while collecting enough blood. Next, we found that Protein Plus, a commercial protein stabilizer, limited hemolysis (a metric for stabilizer efficacy) in venous blood for up to 5 days at 25{degrees}C-35{degrees}C and for 1 day at 40{degrees}C. In addition, we optimized the stabilizer volume and acceptable blood volume range for self-sampling as the stabilizer efficacy is impacted by the stabilizer to blood ratio and collection volume can vary with remote self-sampling devices. Finally, we incubated stabilized blood samples collected via Tasso device at 25{degrees}C-35{degrees}C for 72 h, mimicking a 2-day shipping period. Using a panel of 21 inflammatory proteins, we found that Protein Plus limited intracellular protein release for various proteins (e.g., VEGF-A, CCL11, and IL-8), inhibited protein degradation for CCL2, and enabled minimal hemolysis. These results support Protein Plus as a viable stabilization strategy for remote blood collection technology targeting longitudinal inflammatory protein monitoring.

Circulating mitochondrial DNA (cir-mtDNA) could have a potential comparable to circulating nuclear DNA (cir-nDNA), with numerous applications. However, research and development in this area have fallen behind, particularly considering its origin and structural features. To tackle this, we initially combined Q-PCR and low-pass whole genome sequencing in the same analytical strategy previously and successfully used for cir-nDNA. This revealed unexplained structural patterns and led us to correlate these data with observations made during physical examinations such as filtration, and differential centrifugation in various plasma preparations. Both the integrity index and number of reads revealed a very minor proportion of low size-ranged fragments (<1000 bp) in plasma obtained with a standard preparation (0.06%). Filtration and high speed second step centrifugation revealed that 98.7 and 99.4% corresponded to extracellular mitochondria either free or in large extracellular vesicles. When avoiding platelet activation during plasma preparation, the proportion of both types of entities was still preponderant (76-80%), but the amount of detected mitochondrial DNA decreased 67-fold. In correlation with our previous study on the presence of circulating cell-free mitochondria in blood, our differential centrifugation procedure suggested that cir-mtDNA is also associated with approximately 18% small extracellular vesicles, 1.7% exosomes and 4% protein complexes.

ObjectiveThe objective of the study is to evaluate the accuracy of NGS based quantitative cfDNA analysis for fetal antigen genotyping in alloimmunized pregnancies undergoing clinical testing in practices across the US as early as ten weeks gestational age. Timely identification of the fetal red blood cell antigen genotype for the antigen to which the pregnant person is alloimmunized is vital for determining risk for hemolytic disease of the fetus or newborn (HDFN) and guiding management. The currently recommended approach to assessing risk for HDFN in the US relies on determining the antigen status of the reproductive partner and/or amniocentesis-an approach with limitations such as low uptake and the potential for nonpaternity, which could be alleviated by the utilization of cfDNA testing to determine fetal antigen status. MethodsPatients with alloimmunized pregnancies undergoing clinical fetal antigen cfDNA analysis were recruited to the study along with the neonates resulting from the pregnancies via outreach from the laboratory. The laboratory issued the results prospectively as a part of clinical care. After delivery, neonatal buccal swabs were sent to an outside, independent laboratory for antigen genotyping. The outside laboratory was blinded to the fetal cfDNA results, and the results were compared. Concordance was reported for the fetal antigen cfDNA analysis for antigens to which the pregnant person was alloimmunized as well as for all antigens for which the pregnant person was genotype negative. ResultsA total of 156 participants from 120 clinics who received clinically ordered cfDNA fetal antigen testing provided neonatal buccal swabs for genotyping following delivery. Concordance between cfDNA analysis results and neonatal genotype was determined for 465 antigen calls for the following antigens K1 (n=143), E (124), C (60), Fya (50), c (47), and D(RhD) (41). These 465 calls included 145 where the fetus was antigen positive and 320 where the fetus was antigen negative. We observed complete concordance between prenatal fetal antigen cfDNA analysis results and neonatal genotypes for the 465 calls, resulting in 100% sensitivity, specificity, and accuracy across a racially and ethnically diverse cohort. ConclusionThis study demonstrates that cfDNA analysis for determining fetal antigen genotype is more accurate than real life application of the current recommendations, i.e., partner testing and amniocentesis, in a diverse US population. In addition, this noninvasive approach reduces barriers to obtaining timely and accurate information about fetal antigen genotype. Taken together with previously published evidence, this study supports the implementation of cfDNA testing to manage alloimmunized pregnancies as a clinically useful and cost effective approach.

ObjectiveWe aimed to evaluate the performance of a cell free DNA (cfDNA) assay that uses next generation sequencing (NGS) with quantitative counting templates (QCT) for the clinical detection of the fetal RHD genotype in a diverse RhD-negative pregnant population in the United States (US). Study DesignThis retrospective cohort study was conducted in four US healthcare centers. The same NGS QCT cfDNA fetal RhD assay was offered to non-alloimmunized, RhD-negative pregnant individuals as part of clinical care. Rh immune globulin (RhIG) was administered at the discretion of the provider. The assays sensitivity, specificity, and accuracy were calculated considering the neonatal RhD serology results. ResultsA total of 401 non-alloimunized RhD-negative pregnancies who received clinical care in the period from August 2020 to November 2023 were included in the analysis. The D antigen cfDNA result was 100% concordant with the neonatal serology, resulting in 100% sensitivity, 100% positive predictive value (both 95% CI: 98.6%-100%), 100% specificity, and 100% negative predictive value (both 95% CI: 97.4%-100%). There were 10 pregnancies where the cfDNA analysis identified a non-RHD gene deletion, including RhD{Psi} (n=5) and RHD-CE-D hybrid variants (n=5). RhIG was administered to 93% of pregnant individuals with cfDNA results indicating an RhD-positive fetus compared to 75% of pregnant individuals with cfDNA results indicating an RhD-negative fetus, signifying providers were using the results to guide pregnancy management. ConclusionThis cfDNA analysis via NGS for detecting fetal RhD status is highly accurate with no false-positive or false-negative results in 401 racially and ethnically diverse pregnancies with 100% follow up of all live births. This study and prior studies of this assay support a recommendation to offer cfDNA screening for fetal Rh status as an alternative option to prophylactic RhIG for all non-alloimmunized RhD-negative individuals, which will result in more efficient and targeted prenatal care with administration of RhIG only when medically indicated.

The gold standard for diagnosis of COVID-19 is detection of SARS-CoV-2 RNA by RT-PCR. However, the effect of systematic changes in specimen viral burden on the overall assay performance is not quantitatively described. We observed decreased viral burdens in our testing population as the pandemic progressed, with median sample Ct values increasing from 22.7 to 32.8 from weeks 14 and 20, respectively. We developed a method using computer simulations to quantify the implications of variable SARS-CoV-2 viral burden on observed assay performance. We found that overall decreasing viral burden can have profound effects on assay detection rates. When real-world Ct values were used as source data in a bootstrap resampling simulation, the sensitivity of the same hypothetical assay decreased from 97.59 (95% CI 97.3-97.9) in week 12, to 74.42 (95% CI 73.9-75) in week 20. Furthermore, simulated assays with a 3-fold or 10-fold reduced sensitivity would both appear to be >95% sensitive early in the pandemic, but sensitivity would fall to 85.55 (95% CI 84.9-86.2) and 74.38 (95% CI 73.6-75.1) later in the pandemic, respectively. Our modeling approach can be used to better quantitate the impact that specimen viral burden may have on the clinical application of tests and specimens.