The Analyst

Accurate quantification of HIV-1 p24 antigen depends on the availability of reference materials whose values are traceable to the International Unit (IU). The long-standing WHO International Standard 90/636 is now largely depleted, while access to more recent VLP-based standards remains limited, creating a practical need for locally produced materials supported by a robust and internationally traceable value-assignment procedure. We developed a streamlined, two-component framework for assigning p24 activity to a VLP-derived material using (i) single-point calibration against the WHO standard and (ii) verification through an internal dilution control prepared from the same material. A multi-laboratory study involving 16 laboratories and 37 fourth-generation Ag/Ab assays demonstrated that the approach yields stable relative-response measures and supports the use of robust statistics for activity estimation. Phylogenetic analysis confirmed that the VLP material corresponds to HIV-1 subtype B, ensuring biological alignment with existing WHO standards. The combined procedure produced an assigned value of 32 IU per vial, with dilution-control behaviour indicating correct preparation and consistent analytical parallelism across platforms. This methodology provides a reproducible, resource-efficient route for establishing traceable VLP-based p24 reference materials in settings where access to international standards or large collaborative studies is limited.

Pathogens encapsulate or encode their own suite of enzymes to facilitate replication in the host. The pathogen-derived enzymes possess specialized activities that are essential for pathogen replication and have naturally been candidates for drug targets. Phenotypic assays detecting the activities of pathogen-derived enzymes and characterizing their inhibition under drugs offer an opportunity for pathogen detection, drug resistance testing for individual patients, and as a research tool for new drug development. Here, we used HIV as an example to develop assays targeting the reverse transcriptase (RT) enzyme encapsulated in HIV for sensitive detection and phenotypic characterization, with the potential for point-of-care (POC) applications. Specifically, we targeted the complementary (cDNA) generation activity of the HIV RT enzyme by adding engineered RNA as substrates for HIV RT enzyme to generate cDNA products, followed by cDNA amplification and detection facilitated by loop-mediated isothermal amplification (LAMP) or CRISPR-Cas systems. To guide the assay design, we first used qPCR to characterize the cDNA generation activity of HIV RT enzyme. In the LAMP-mediated Product-Amplified RT activity assay (LamPART), the cDNA generation and LAMP amplification were combined into one pot with novel assay designs. When coupled with direct immunocapture of HIV RT enzyme for sample preparation and endpoint lateral flow assays for detection, LamPART detected as few as 20 copies of HIV RT enzyme spiked into 25L plasma (fingerstick volume), equivalent to a single virion. In the Cas-mediated Product-Amplified RT activity assay (CasPART), we tailored the substrate design to achieve a LoD of 2e4 copies (1.67fM) of HIV RT enzyme. Furthermore, with its phenotypic characterization capability, CasPART was used to characterize the inhibition of HIV RT enzyme under antiretroviral drugs and differentiate between wild-type and mutant HIV RT enzyme for potential phenotypic drug resistance testing. Moreover, the CasPART assay can be readily adapted to target the activity of other pathogen-derived enzymes. As a proof-of-concept, we successfully adapted CasPART to detect HIV integrase with a sensitivity of 83nM. We anticipate the developed approach of detecting enzyme activity with product amplification has the potential for a wide range of pathogen detection and phenotypic characterization.

Accurate detection and quantification of HIV-1 proviral DNA are critical for effective patient monitoring and therapeutic decision-making. In this study, we developed a multiplexed quantitative PCR (qPCR) assay designed to detect HIV-1 proviral DNA, determine viral subtype, specifically identifying the predominant subtype C and validate assay performance using an internal control. Gene-specific primers were engineered by appending an 8-base biotag followed by a common 18-base sequence at the 5' end, enabling the simultaneous amplification of multiple target sequences. Fluorescent probes labeled with FAM, SUN/VIC, and Cy5 were employed for detection, and a novel strategy involving quenching of labeled probes in trans was implemented to enhance assay flexibility and cost-effectiveness compared to conventional cis-quenched probes. The assay was initially optimized using synthetic linear double-stranded DNA templates representing the HIV-1 gag region, while externally added human chromosomal DNA served as a control for PCR inhibition. Validation was performed on a panel of 11 clinical samples previously analyzed for drug resistance mutations. Results indicated robust amplification of HIV-1 proviral DNA, accurate subtype determination, and reliable internal control performance, with profiles closely matching those obtained by gold standard sequencing-based assays. One sample exhibited PCR inhibition, underscoring the need for internal control monitoring. Overall, the multiplexed qPCR assay provides a sensitive, specific, and efficient tool for comprehensive HIV-1 reservoir quantification and molecular epidemiological studies, potentially informing improved clinical management and personalized treatment strategies. Furthermore, this novel methodology significantly reduces reagent costs and processing time while maintaining high sensitivity, making it ideal for routine clinical and research applications.

The estimation of the biological sex of archeological remains is crucial information in bioarchaeology and forensic anthropology. In recent years, proteomics based on molecular sexual dimorphism have emerged as a preferred method, particularly because of its minimally-invasive approach to extracting amelogenin X and Y proteins from tooth enamel. However, there is an increasing demand to accelerate this process while facilitating the analysis of large archaeological assemblages. This study presents a novel high-throughput targeted paleoproteomics method for biological sex estimation using MALDI-CASI-FTICR mass spectrometry. This approach combines the strengths of existing methods, including ultra-high resolution, significantly reduced processing times, targeted analysis, and scalability to large archaeological sample sets. The method was initially validated on modern individuals with known sex and subsequently applied to 130 adult and juvenile individuals from medieval Great Moravia (present-day Czech Republic). Biological sex was successfully estimated for all but one of the individuals. The results not only provide a more efficient biological sex estimation but also help to resolve a few errors in sex assessment previously encountered with osteomorphological and tooth morphometric techniques. The implementation of this method significantly improves the accuracy and efficiency of biological sex estimation, offering a powerful tool for anthropological research. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/706309v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1ede7e6org.highwire.dtl.DTLVardef@13d2f5org.highwire.dtl.DTLVardef@17ee44dorg.highwire.dtl.DTLVardef@1be9dd9_HPS_FORMAT_FIGEXP M_FIG C_FIG

The availability of effective antiretroviral therapy has made HIV manageable, provided patients have consistent access to routine viral load (VL) testing. Nonetheless, access to frequent VL testing remains limited. There is a need for accessible, user-friendly testing systems that allow people living with HIV (PLHIV) to monitor their VL more frequently and empower self-management. Here, we developed ViraLite, a sample-to-answer, compact, accessible, and battery-powered system for HIV viral load monitoring. The system is built upon a probe-based RT-LAMP assay that allows for multiplexed detection and quantification. An internal quality control targeting the RNase P was incorporated to enhance the reliability of the results. A software-reconfigurable real-time sensing system empowered by machine learning and a smartphone-guided protocol was developed in tandem to analyze the multiplexed assay. We analyzed 45 clinically archived samples using ViraLite and benchmarked our results against qRT-PCR, which showed 21 positive and 23 negative samples. Using our process control, ViraLite first identified 17 inconclusive samples that would otherwise be classified as negative. Then, ViraLite classified 14 out of 15 HIV-positive samples (93.3%) and 13 out of 13 HIV-negative samples (100%). The incorporation of RNase P as a process control increased the sensitivity of ViraLite from 66.66% to 93.33%, while maintaining a high specificity (100%). To assess the acceptance of ViraLite among PLHIV, we recruited 480 participants from online and three clinical sites to complete a survey. Over 86% of participants indicated ViraLite had benefits in convenience and privacy, on the other hand 61% of participants indicated concerns with test accuracy. The integration of compact hardware, a reliable assay, and smartphone guidance provides an accurate, easy to use system for PLHIV to self-manage their viral load and update their prescriptions frequently.

Early detection of HIV-1 infection is crucial to initiate anti-retroviral therapy (ART) to suppress viremia and disease progression. Herein, we developed a CRISPR/Cas12a-based HIV-1 detection assay by optimizing components for a coupled isothermal preamplification by recombinase polymerase amplification (RPA). The HIV-1 Indian Clade-C-specific conserved pol region was targeted by crRNA designed for Clade-specific detection. The CRISPR/Cas12a cleavage of the viral cDNA input is displayed as a single visually detectable outcome due to the collateral cleavage of the ssDNA-FAM-BQ reporter, enabling the rapid detection of HIV-1. The performance of the assay was evaluated by testing sera of 41 Indian Clade C HIV-1 seropositive individuals, which included 28 HIV-1 infected infant samples, HIV-1 Indian clade C genome plasmid, viral disease control DNA/RNA samples (Influenza, RSV, Parvovirus, HPIV, CMV, and HBV), and 31 healthy donor sera samples. With 96% sensitivity and 92.65% specificity for HIV-1C detection, with fluorescence and visual readout, and a capability of detection using lateral flow dipsticks, our CRISPR/Cas12a-based HIV-1 C detection assay demonstrates the potential to be developed into a robust point-of-care molecular diagnostic test for HIV-1C. Moreover, it may serve as a potential rapid NAT alternative in detecting mother-to-child transmission (MCT) of HIV-1C in infants (<2 years of age), where rapid antibody-based serology tests are rendered ineffective due to the presence of maternal antibodies.

Recently developed secondary nanobodies or single-domain antibodies present a powerful tool for immunodetection. Unlike traditional antibodies, their monovalence enables pre-association with primary antibodies prior to sample staining, presenting a straightforward affinity-based antibody labeling solution. This not only simplifies and streamlines immunoassays, it also supports multiplexed techniques where conflicts in the species of the desired primary antibodies preclude standard indirect immunostaining. Despite these advantages, the use of secondary nanobodies remains sparse, due perhaps to a lack of evaluation on their suitability for assays requiring quantification and an assessment of optimal protocols for their use. Here, we present a set of experiments spanning single-molecule detection to cell imaging that can be used to validate secondary nanobody binding, specificity, and their propensity for mis-targeted binding in multiplex assays. Using these tools, we analyzed the binding properties of commercially available secondary nanobodies and outline optimized protocols for their robust use.

BackgroundFormalin-fixed, paraffin-embedded (FFPE) tissue remains the gold standard for extensively archiving biological specimens, providing biobanks with large repositories of retrospective potential. However, while formalin crosslinking is effective at preserving tissue, it poses significant challenges for extracting molecular information, including the proteome. Traditionally, this process required high levels of input material, which, in turn, limited the ability to preserve cell-type heterogeneity and spatial information. To address these limitations, we developed an easily adaptable and highly efficient workflow for extracting deep proteomes from low-input materials, such as biopsies used in routine histopathological diagnostics. MethodsWe compared the extraction efficiency of pancreatic acinar cells identified in FFPE tissue samples stained with conventional hematoxylin-eosin (H&E) against that of cells isolated from tissue samples immunostained for the epithelial cell adhesion molecule (EpCAM) across material inputs ranging from 1,166 to 800,000 {micro}m2 (estimated to 2 to 1,310 cells in volume). Cells were isolated using laser capture microdissection and subsequently analyzed using Liquid Chromatography-Tandem Mass Spectrometry. ResultsSimilar yields for both methods were observed, with EpCAM-positive cells yielding slightly higher results--approximately 1,200 unique protein groups at the lowest input and up to 5,900 at the highest. In cells isolated from H&E-stained tissue, [~]900 to [~]5,200 protein groups were identified. We decided that the optimal balance for our workflow, ensuring maximum protein identification while minimizing input material, lies within the range of approximately 50,000 to 100,000 {micro}m2. With these results, we tested spatial capabilities and biological relevance by isolating cancer cells from biopsies of pancreatic cancer, lung cancer, or glioblastoma, with the first two being stained with EpCAM and the latter being stained against the tumor-suppressor protein p53. We successfully identified tissue-specific protein expressions and observed prominent clustering of all cell populations. DiscussionOur results highlight the feasibility of performing spatial proteomics on FFPE tissue using minimal input material. This adaptable methodology opens up possibilities for investigating cell-type-specific biology while preserving spatial and histological information.

1.BackgroundHuman infertility affects approximately 17.5% of the global population, with male factors accounting for nearly half of all cases. The identification of reliable molecular biomarkers is crucial for improving the diagnosis and assessment of male fertility. In this study, we developed and optimized an untargeted high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (HPLC-ESI-MS/MS) workflow for comprehensive lipidomic and metabolomic profiling of human spermatozoa using only 1.25 million cells per sample. ResultsCompared to previous reports, our optimized method achieved unprecedented analytical depth, identifying 473 lipid species and 955 structurally annotated metabolites, corresponding to nearly 7.600-fold improvements in detection efficiency per cell over published approaches. Lipidomic analysis revealed cholesterol, fatty acids, phosphatidylcholines, and phosphatidylethanolamine plasmalogens as the most abundant lipid classes, consistent with the structural complexity of the sperm plasma membrane. Metabolomic profiling showed strong enrichment of lipid-related and steroidogenic pathways, including phospholipid biosynthesis, glycerolipid metabolism and androgen and estrogen metabolism. The integration of lipidomic and metabolomic data highlighted functionally interconnected pathways related to membrane dynamics, energy metabolism, and hormone biosynthesis. ConclusionsOverall, this work establishes a robust, sensitive, and scalable analytical framework enabling high-coverage molecular characterization of spermatozoa from limited sample material, laying the groundwork for future biomarker discovery and clinical applications in male infertility research. One Sentence SummaryDevelopment of a highly sensitive untargeted HPLC-ESI-MS/MS lipidomic and metabolomic workflow that achieves unprecedented molecular coverage from only 1.25 million human spermatozoa, revealing interconnected lipid and metabolic pathways and providing a robust foundation for biomarker discovery in male infertility. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/703749v1_ufig1.gif" ALT="Figure 1"> View larger version (74K): org.highwire.dtl.DTLVardef@10b3132org.highwire.dtl.DTLVardef@1caf850org.highwire.dtl.DTLVardef@746adborg.highwire.dtl.DTLVardef@1135539_HPS_FORMAT_FIGEXP M_FIG C_FIG

There is a constant pressure to reduce timelines in mammalian cell line development (CLD) for biotherapeutic protein production. Demonstration of clonal derivation of the generated cell lines is key for health authorities approval. To meet these regulatory and process-oriented demands, single-cell dispensers have become vital instruments for single-cell cloning. We conducted validation experiments with the UP.SIGHT (CYTENA GmbH) to determine this instruments single-cell dispensing efficiency (SCDE) and probability of clonal derivation (p(clonal)). Process optimization to maximize clone recovery with several cell lines was also performed, focusing on cloning media and plate type. With a SCDE >97%, p(clonal) >99.99% and clone recovery values of up to 80%, the data reported here support the notion that the UP.SIGHT covers all steps in the single-cell dispensing process with assurance of clonality and colony tracking, leading to faster and more efficient CLD workflows. This work also serves as a guideline for instrument validation and guidance towards process optimization.

-Synuclein seed amplification assays are a promising diagnostic tool for synucleinopathies such as Parkinsons disease and multiple system atrophy. Standardized conditions are required to ensure a high degree of inter- and intra-laboratory reproducibility when performing these assays. A significant issue that hinders the utility of seed amplification assays is the de novo aggregation propensity of the -synuclein substrate as well as inter-batch heterogeneity. While much work has focused on determining appropriate seed amplification assay buffer compositions as well as the type and amount of seed used, a robust comparison of -synuclein substrate purification methods has not been reported. We therefore compared the utility of recombinant -synuclein purified using four different methods as seed amplification assay substrates across two laboratories. Osmotic shock-purified -synuclein monomer substrate showed the lowest propensity for de novo aggregation, which translated into being the best substrate for seed amplification assay reactions seeded with -synuclein preformed fibrils or patient brain homogenates. Furthermore, osmotic shock -synuclein monomer showed the best inter-batch reproducibility compared to all other substrates tested. As -synuclein seed amplification assays continue to evolve and move towards adoption in the clinical realm, this work showcases the vital importance of standardizing the production and characterization of recombinant -synuclein substrate. We encourage the widespread adoption of osmotic shock -synuclein monomer as the universal substrate for seed amplification assays to maximize intra- and inter-laboratory reproducibility.

Here we present a rapid and versatile method for capturing and concentrating SARS-CoV-2 from transport medium and saliva using affinity-capture magnetic hydrogel particles. We demonstrate that the method concentrates virus prior to RNA extraction, thus significantly improving detection of the virus using a real-time RT-PCR assay across a range of viral titers, from 100 to 1,000,000 viral copies/mL; in particular, detection of virus in low viral load samples is enhanced when using the method coupled with the IDT 2019-nCoV CDC EUA Kit. This method is compatible with commercially available nucleic acid extraction kits, as well with a simple heat and detergent method. Using transport medium diagnostic remnant samples that previously had been tested for SARS-CoV-2 using either the Abbott RealTime SARS-CoV-2 EUA Test (n=14) or the Cepheid Xpert Xpress SARS-CoV-2 EUA Test (n=35), we demonstrate that our method not only correctly identifies all positive samples (n = 17) but also significantly improves detection of the virus in low viral load samples. The average improvement in cycle threshold (Ct) value as measured with the IDT 2019-nCoV CDC EUA Kit was 3.1; n = 10. Finally, to demonstrate that the method could potentially be used to enable pooled testing, we spiked infectious virus or a confirmed positive diagnostic remnant sample into 5 mL and 10 mL of negative transport medium and observed significant improvement in the detection of the virus from those larger sample volumes.

According to WHO report 39.0 million (33.1-45.7 million) people around the world were living with HIV infection till Dec of 2022. One method that we need for treatment and monitoring of HIV patients is CD4 counting and CD4/CD8 ratio. For this purpose, there are many different laboratory test with technical complexity and in other hand most of them are time consuming, expensive or cannot be used in resource limited places. This work demonstrates POCT microchip platform for enumerating leukocytes, CD4+ T- lymphocytes, and CD8+ T lymphocytes from whole blood, using fluorochrome-conjugated primary antibodies as a detection method. This device and its method omits all obstacles for WBC and CD4, CD8 T lymphocytes and it offers fast, cost effective and easy absolute WBC and CD4, CD8 T lymphocytes count for monitoring HIV patients immune situation with high accuracy which can be implemented insource limited stings or doctors office. We incubated Phycoerythrin (PE) conjugated primary antibodies specific to CD4 and CD8 antigens to enumerate CD4+ and CD8+ T cells, respectively. Comparison studies were performed with FACS count to evaluate total leukocytes, CD4+T cell number, and CD8+T cell number in whole blood samples for monitoring the immune systems of patients with human immunodeficiency virus (HIV)/AIDS. Statistical analyses for precision, correlation, and agreement were performed. Coefficients of variation (CV) ranging from 0.67% to 12.78%, 0.81 to 13.68%, and 0.29% to 8.33% were obtained for CD4, CD8 and leukocyte recovery respectively. A significant correlation was found between the two assays for CD4 count and CD8 count, with correlation coefficients of 0.90 and 0.91, respectively. Using Bland-Altman plots, a mean bias of 23, 38, and 490 cells/{micro}L (95% CI, n=113) was obtained for CD4, CD8, and total leukocyte count, respectively. These data show that the GBTsol ICA (Immune Cell Analyzer) is comparable to the FACS count platform method for measuring the amounts of CD4 T cells, CD8 T cells, and total leukocytes in blood samples for the purpose of monitoring HIV/AIDS patients with cheap, easy and fast way.

Raman spectroscopy is a popular tool for characterizing complex biological materials and their geological remains1-10. Ordination methods, such as Principal Component Analysis (PCA), rely on spectral variance to create a compositional space1, the ChemoSpace, grouping samples based on spectroscopic manifestations that reflect different biological properties or geological processes1-7. PCA allows to reduce the dimensionality of complex spectroscopic data and facilitates the extraction of relevant informative features into data formats suitable for downstream statistical analyses, thus representing an essential first step in the development of diagnostic biosignatures. However, there is presently no systematic survey of the impact of sample, instrument, and spectral processing on the occupation of the ChemoSpace. Here the influence of sample count, signal-to-noise ratios, spectrometer decalibration, baseline subtraction routines, and spectral normalization on ChemoSpace grouping is investigated using synthetic spectra. Increase in sample size improves the dissociation of sample groups in the ChemoSpace, however, a stable pattern in occupation can be achieved with less than 10 samples per group. Systemic noise of different amplitude and frequency, features that can be introduced by instrument or sample11,12, are eliminated by PCA even when spectra of differing signal-to-noise ratios are compared. Routine offsets ({+/-} 1 cm-1) in spectrometer calibration contribute to less than 0.1% of the total spectral variance captured in the ChemoSpace, and do not obscure biological information. Standard adaptive baselining, together with normalization, increase spectral comparability and facilitate the extraction of informative features. The ChemoSpace approach to biosignatures represents a powerful tool for exploring, denoising, and integrating molecular biological information from modern and ancient organismal samples.

There is a significant global health need to translate more in vitro diagnostic tests (IVDs) from clinical laboratories to field-based applications, including point-of-care (POC) and self-administered test formats. These applications typically require smaller sample sizes, limit the extent of sample processing and measurement capabilities, and introduce greater handling variability. Error tolerance is one of the most critical factors for successful field-based assay design. Here, we examine machine-learning (ML) strategies to enhance the error tolerance of image-based nanoparticle immunoassays. Random dispersions of nanoparticles were imaged in microliter sample volumes, and images were processed to determine analyte concentrations based on nanoparticle appearance. Assay performance was characterized using two common blood diagnostics: C-reactive protein (CRP) and S.CoV-2 IgG. We compare the results from a conventional image analysis, a hybrid ML-conventional approach based on pixel segmentation, and a full end-to-end image regression using a targeted regularization strategy. Training images for the full image regression approach required only a single label for training - the analyte concentration - eliminating the need for labor-intensive pixel-level labeling. Ultimately, the fully ML-based analysis significantly improved dynamic range, sensitivity, and reproducibility in high-error settings, including direct measurements performed in whole blood.

AbstractGaining control of existing biomanufacturing chassis organisms, such as Escherichia coli K12, and novel isolates, such as the salt tolerant Halomonas bluephagenesis sp TD01 studied here may be facilitated by the investigation and monitoring of their metabolic and regulatry processes, particularly through proteomics. Here we consider the performance of a range of typically available proteomics platforms across a range of price points to map chasis organisms metabolic pathways. A set of model bacterial samples was prepared from E. coli and H. bluephagenesis sp. TD01 in 1:2 and 2:1 ratios and analysed using five LC-MS systems. Data from the timsTOF HT, Exploris 480, ZenoTOF 7600 and Select Series MRT were processed through DIANN and MSStats. Data from the Vion was processed through Skyline then MSstats. Of the 8,222 proteins identified across all samples analysed (4,401 proteins from E. coli; 3,821 from Halomonas sp. TD01), the TimsTOF and Exploris were able to achieve extensive proteome coverage quantifying 5.5k and 5k proteins respectively, with the ZenoTOF, Waters MRT and the legacy Waters Vion respectively quantifying 3.5k, 1.3k, and [~]850 proteins at 1% FDR. Proteins comprising the pathways of chassis organisms core metabolism critical to biomanufacturing were quantified with all instruments, demonstrating suitability of these platforms to explore their manipulation in the context of biomanufacturing.

Analytical chemistry provides the content of nearly every scientific, technical and business decision relating to what atoms, molecules and devices are and how they interact in their environment. Various workflows come together in describing those interactions--separation, identification, quantitation, classification--to inform decisions across society. Each workflow has associated equipment, infrastructure and subject matter experts that integrate results for input into those decisions. However, those existing workflows are constrained by their evolution and often require integrated applications of the multiple analytical techniques integrated to form conclusions, thereby increasing cost, complexity and quality. This paper describes an alternative approach to addressing the many limitations of contemporary analytical chemistry. Here machine learning is leveraged to analyze high dimensional data derived from a nano-electrochemical sensor platform, to perform classification, identification and quantitation analyses in an integrative manner from a single-shot measurement. The approach described here for biological samples does not require sample-specific preparation or marker-specific probes and labels to identify and/or quantitate the analyte of interest, thereby liberating the physical measurement of the sample from chemicals and workflows that are tied to the underlying biochemical hypothesis. The nano-electrochemical sensor, as described in a prior publication, transduces the vibrational frequencies of multiple species in the sample into a collection of discrete electronic signatures that represent the sample in a high dimensional space, while using only 2-4{micro}l sample volume. The sample analysis relies on training data to identify features in the high dimensional input dataset that are unique to the molecule or phenotype under consideration and distinct from the sample background matrix and/or a set of defined sample controls. The molecule- or phenotype-associated features can be tracked with clinically identified disease burden in the patient or with biomolecule concentration in the biological sample. The identification of patients with dementia from a plasma test is outlined here to demonstrate the applicability of this approach to classifying phenotypes associated with complex neurological disorders in a marker-agnostic manner. This approach is applied to a specific and quantitative assay of insulin Humalog and insulin Toujeo in a batch that comprises of a mixture of the two peptides, where Toujeo differs from Humalog in three amino acids residues only. We also describe the development of an assay for IL-6 in spiked human plasma samples with this method, thereby demonstrating the applicability of this analysis paradigm to more complex samples and protein-like species.

Lyme Disease is a multisystem infectious disease caused by the Borellia burgdorferi complex, and is a growing threat to public health. Approximately 476,000 people are infected with Lyme in the United States each year. Although Lyme is readily treated with antibiotics when detected early, early detection remains difficult. Current testing remains difficult because the standard 2-tiered ELISA/Western assay indirectly detects Lyme via measurement of a host immune response, which suffers from an inherent time-lag in host antibody production. A direct test for Lyme Disease would overcome these inherent limitations. To this end we report on the first microfluidic immunocapture device for Lyme Disease. We engineered a geometrically enhanced differential immunocapture (GEDI) technology to capture whole-organism Borrelia for direct on-chip detection. This approach is potentially amenable with other work in the field to develop direct PCR or aptamer tests for Lyme Disease, as our device could serve as a platform to drastically enhance the concentration of present Borrelia into a small volume.

Combining different "omics" approaches, such as genomics and proteomics, is necessary to generate a detailed and complete insight into microbiome comprehension. Proper sample collection and processing and accurate analytical methods are crucial in generating reliable data. We previously developed the ChipFilter device for proteomic analysis of microbial samples. We have shown that this device coupled to LC-MS/MS can successfully be used to identify microbial proteins. In the present work, we have developed our workflow to analyze concomitantly proteins and nucleic acids from the same sample. We performed lysis and proteolysis in the device using cultures of E. coli, B. subtilis, and S. cerevisiae. After peptide recovery for LC-MS/MS analysis, DNA from the same samples was recovered and successfully amplified by PCR for the 3 species. This workflow was further extended to a complex microbial mixture of known compositions. Protein analysis was carried out, enabling the identification of more than 5000 proteins. The recovered DNA was sequenced, performing comparable to DNA extracted with a commercial kit without proteolysis. Our results show that the ChipFilter device is suited to prepare samples for parallel proteomic and genomic analyses, which is particularly relevant in the case of low-abundant samples and drastically reduces sampling bias.

The study of ancient proteins preserved in a range of archaeological, cultural heritage, and palaeontological materials is increasingly contributing to our understanding of human evolution and archaeological research questions. Many of the specimens studied have been excavated and stored for a significant duration prior to their proteomic analysis. Human handling and storage environments therefore provide ample opportunities for protein contamination onto and into specimens of interest to palaeoproteomic studies. As such, modern protein contamination limits access to endogenous proteomes. Here, we compare five approaches of bone protein decontamination applied to a Pleistocene Equus sp. bone fragment contaminated with a modern dog salivary proteome. We find that all tested methods reduce the protein contamination, but with different efficiencies. We find that a brief bleach wash is the most effective approach in removing modern protein contamination, and that no additional damage is caused to the endogenous proteome by this treatment. Next, we apply this approach to a hominin tooth found at Khudji, a Late Pleistocene archaeological site in Tajikistan. We demonstrate that a brief bleach wash removes almost all human skin protein contamination while retaining the endogenous hominin dentine proteome. Subsequent phylogenetic analysis of the Khudji dentine proteome allowed determination that the specimen is likely not a Denisovan, but still leaves ambiguity between an assignment to either modern humans or Neanderthals.