Analytica Chimica Acta

The crucial role of Dendritic Cells (DCs) in anti-tumor immune responses depends on surface Sialic Acid (SA). The regulation of DC surface sialic acid in the Tumor Microenvironment (TME) remains underexplored. Current methods struggle to provide highly sensitive, multiplex analyses of SA and other immune protein changes in single DCs within the tumor microenvironment. Here, we employed a SERS tags method for specific and highly sensitive analysis of SA, MHCII, CD86, and CD40 in DCs using a single DC analysis microfluidic platform. We also explored the differential regulatory effects of various immune-modulating drugs and tumor supernatant on multiple immunophenotypes of DCs in different immune states. The expanded two-cell microfluidic system further allows for phenotypic analysis of DCs at different time points within the tumor microenvironment. Given this method enables highly sensitive single-cell analysis of DCs, further development of this technology for tumor microenvironment applications will aid in deeply understanding the tumor-induced suppression of DC immune function, providing valuable insights for DC-mediated tumor immunotherapy research.

Metastatic cervical lymph nodes (LN) are detected in 20-30% of patients with differentiated thyroid cancer (DTC). Current guidelines recommend that once a cervical LN is suspected to be DTC metastasis during a neck ultrasound (US) procedure, it should be investigated via a fine needle aspiration (FNA) biopsy for cytological evaluation and saline washout of the needle for thyroglobulin (Tg) measurement (FNA-Tg). Since Tg is a protein produced exclusively by thyroid follicular cells, a positive FNA-Tg result establishes the diagnosis of metastatic DTC irrespective of cytology. The conventional, immunoassay-based, FNA-Tg washout requires a laboratory and skilled personnel. We developed a semi-quantitative, lateral flow-based method which was shown to detect at the point-of-care (POC), within 10 minutes, positive Tg samples in needle washouts of a suspicious LN at the site of FNA biopsy. In the pre-clinical phase, the POC-Tg limit of detection was determined to be at a concentration equal to 5 ng/mL, after a 1 mL dilution with normal saline. Our prototype was optimized by evaluating different components: types of membranes, pads, antibodies, and gold conjugates. We evaluated our POC-Tg kits on thirty clinical samples: 16 were found positive while the other 14 were seen as negative. All the negative and positive results were further validated by the attending clinical labs, resulting in 100% compatibility compared to the standard procedure. The proof-of-value of our POC-Tg test lies in its ability to significantly reduce the time to results, thus enhancing clinical decision-making, and saving time and valuable resources.

Imaging mass spectrometry (IMS) provides promising avenues to augment histopathological investigation with rich spatio-molecular information. We have previously developed a classification model to differentiate melanoma from nevi lesions based on IMS protein data, a task that is challenging solely by histopathologic evaluation. Most IMS-focused studies collect microscopy in tandem with IMS data, but this microscopy data is generally omitted in downstream data analysis. Microscopy, nevertheless, forms the basis for traditional histopathology and thus contains invaluable morphological information. In this work, we developed a multimodal classification pipeline that uses deep learning, in the form of a pre-trained artificial neural network, to extract the meaningful morphological features from histopathological images, and combine it with the IMS data. To test whether this deep learning-based classification strategy can improve on our previous results in classification of melanocytic neoplasia, we utilized MALDI IMS data with collected serial H&E stained sections for 331 patients, and compared this multimodal classification pipeline to classifiers using either exclusively microscopy or IMS data. The multimodal pipeline achieved the best performance, with ROC-AUCs of 0.968 vs. 0.938 vs. 0.931 for the multimodal, unimodal microscopy and unimodal IMS pipelines respectively. Due to the use of a pre-trained network to perform the morphological feature extraction, this pipeline does not require any training on large amounts of microscopy data. As such, this framework can be readily applied to improve classification performance in other experimental settings where microscopy data is acquired in tandem with IMS experiments.

Digital polymerase chain reaction (PCR) is a fast-developed technology, which makes it possible to provide absolute quantitative results. However, this technology has not been widely used in research field or clinical diagnostics. Although digital PCR has been born for two decades, the products on this subject still suffer from either high cost or cumbersome user experience, hence very few labs have the willingness or budget to routinely use such product; On the other hand, the unique sensitivity of dPCR over traditional qPCR shows great potential applications. Here, a cost-effective digital PCR method based on a microfluidic printing system was introduced, trying to overcome those shortcomings. The microfluidic droplet printing technology was utilized in this study to directly generate droplet array containing PCR reaction solution onto the simple glass substrate for the subsequent PCR and imaging, which could be done with any regular flat-panel PCR machine and microscope. The method introduces a new perspective in droplet-based digital PCR in that the droplets generated with this method aligns well in an array without touch with each other, therefore the regular glass and oil could be used without any special surfactant. With simple analysis, the data generated with this method showed reliable quality, which followed the Poisson distribution trend. Compared with other expensive digital PCR methods, this system is more affordable and simpler to integrate, especially for those biological or medical labs which are in need for the digital PCR options but short in budget. Therefore, this method is believed to have the great potential in the future market application.

We demonstrate the first viscoelastic-based flow-integrated Raman spectroscopy (FIRS) platform using spontaneous Raman spectroscopy with a simplified fluidic design that precisely controls particle transit speed and predicts arrival time to the Raman interrogation region, together with time synchronized triggered acquisition. Using viscoelastic flow focusing and optical detection of velocity for hardware triggering, the setup enables high-throughput, real-time Raman analysis of particles in continuous flow. The system operates with a single pressure pump, two sets of fiber-coupled lasers and detectors, and a microcapillary precisely aligned in a projection micro-stereolithography 3D-printed mount, and ensures detection stability across various flow rates. The demonstrated platform is modular and scalable. Detection rates of up to 80 cells per minute were achieved and dinoflagellate Alexandrium ostenfeldii cultures were analyzed. The integrated optical trigger mechanism supports future extensions such as cell sorting or additional analyses. A user-friendly graphical interface provides full control of fluidics, spectroscopy, and real-time monitoring, making this compact, low-cost FIRS platform accessible across a wide range of laboratory environments.

The identification and subsequent characterization of unknown analytes using mass spectrometry presents a long-standing challenge across many research fields, particularly when analyte levels are low and the compound class is underrepresented in mass spectral databases. We have developed a data analysis workflow for investigating classes of small molecules and demonstrated its application through the reanalysis of data collected to probe for modified nucleoside MR1-presented antigens. We reanalyzed the datasets to screen for additional classes of compounds within the MR1 ligandome using Compound Discoverer, a commercial software package designed for metabolomic analysis, featuring fragmentation filtering nodes, molecular networking, and spectral database searching. Our study identified two compound classes that bind to MR1. One class includes compounds characterized by the presence of a ribityl substructure and molecular formulas consistent with structural similarity to riboflavin, where the most abundant compound differs from riboflavin by two additional oxygen atoms and one fewer carbon atom. A second class comprises an adenosine monophosphate isomer and larger analytes that are putatively identified as consisting of di- and tri-covalently bound nucleotides. The application of our analytical approach to characterize the MR1 ligandome demonstrates the power of combining compound-class fragmentation, molecular networking, and mass spectral database searching in exploring receptor ligandomes and, more generally, identifying novel classes of compounds.

Pathological histology is the clinical gold standard for cancer diagnosis. Incomplete or excessive sampling of the formalin-fixed excised cancer specimen will result in inaccurate histology assessment or excessive workload. Conventionally, pathologists perform specimen sampling relying on naked-eye observation which is subjective and limited by human perception. Precise identification of tumor beds, size, and margin is challenging, especially for lesions with inconspicuous tumor beds. To break the limits of human eye perception (visible: 400-700 nm) and improve the sampling efficiency, in this study, we propose using a second near-infrared window (NIR-II: 900-1700 nm) hyperspectral imaging (HSI) system to assist specimen sampling on the strength of the verified deep anatomical penetration and low scattering characteristics of the NIR-II optical window. We use selected NIR-II HSI narrow bands to synthesize color images for human eye observation and also apply artificial intelligence (AI)-based algorithm on the complete NIR-II HSI data for automatic tissue classification to assist doctors in specimen sampling. Our study employing 5 pathologists, 92 samples and 7 cancer types shows that NIR-II HSI-assisted methods have significant improvements in determining tumor beds compared with conventional methods (Conventional color image with or without X-ray). The proposed system can be easily integrated into the current workflow, and has high imaging efficiency and no ionizing radiation. It may also find applications in intraoperative detection of residual lesions and identification of different tissues.

In microbial research, the heterogeneity phenomenon is closely associated with microbial physiology in multiple dimensions. For now, A few studies were proposed in transcriptome and proteome analysis to discover the heterogeneity among single cells. However, microbial single cell metabolomics has not been possible yet. Herein, we developed a method, RespectM, based on discontinuous mass spectrometry imaging, which can detect more than 700 metabolites at a rate of 500 cells per hour. While ensuring the high throughput of RespectM, it integrates matrix sublimation, QC-based peak filtering, and batch correction strategies to improve accuracy. The results show that RespectM can distinguish single microbial cells from the blank matrix with an accuracy of 98.4%, depending on classification algorithms. Furthermore, to verify the accuracy of RespectM for distinguishing different single cells, we performed a classification test on Chlamydomonas reinhardtii single cells among allelic strains. The results showed an accuracy of 93.1%, which provides RespectM with enough confidence to perform microbial single cell metabolomics analysis. As we expected, untreated microbial cells will spontaneously undergo metabolic grouping coherence with genetic and biochemical similarities. Interestingly, the pseudo-time analysis also provided intuitive evidence on the metabolic dimension, indicating the cell grouping is based on microbial population heterogeneity. We believe that the RespectM can offer a powerful tool in the microbial study. Researchers can now directly analyze the changes in microbial metabolism at a single-cell level with high efficiency.

Instant micro- and macro-preparative droplet reaction assisted by hand shaking (shake reaction) was found and observed, where reactions were carried out inside aqueous droplets dispersed in an organic solvent. This shake reaction was successfully benchmarked with both organic reactions of protein reduction by dithiothreitol and alkylation by iodoacetamide as well as biological reaction of protein digestion by enzyme trypsin. The reaction time of shake reaction was substantially shortened from the traditional bulk solution reactions, specifically 1 min for reduction (from 20 min), 1 min for alkylation (from 30 min), 5 min for digestion (from 960 min). The high efficiency of shake reaction comes from the micro-nano droplet formation assisted by shaking. Shake reaction can arguably be extended to any liquid-phase inorganic, organic and biological reactions from micro- to macro-preparative scale, and thus undoubtedly find wide applications in both academic research and industry production. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=161 SRC="FIGDIR/small/651440v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@ad1414org.highwire.dtl.DTLVardef@1014a1dorg.highwire.dtl.DTLVardef@1a8eccdorg.highwire.dtl.DTLVardef@15297fe_HPS_FORMAT_FIGEXP M_FIG C_FIG

Aerosols contain human pathogens that cause public health disasters such as tuberculosis (TB) and the ongoing COVID-19 pandemic. The current technologies for the collection of viruses and microorganisms in aerosols face critical limitations, necessitating the development of a new type of sampling system to advance the capture technology. Herein, we presented a new type of collection system, which exploits the affinity between carbon chains and organic molecules on the surfaces of viruses and microorganisms. We demonstrated that the physical capture efficiency of the collection system was over 99% for particle sizes from 0.3 to 10 {micro}m. We further evaluated the biochemical capture efficiency of the collection system using mass spectrometry approaches and showed that the biochemical information of viruses and microorganisms was well preserved. Coupled with well-established molecular technologies, this new type of capture technology can be used for the investigation of aerosol-related disease transmission models, as well as the development of innovative screening and monitoring tools for human diseases and public health issues.

Rapid, on-site, airborne virus detection is a requirement for timely action against the spread of air-transmissible infectious diseases. This applies both to future threats and to common viral diseases, such as influenza and COVID-19, which hit vulnerable populations yearly with severe consequences. The ultra-low concentrations of virus in the air make airborne virus detection difficult, yet readily infect individuals when breathed. Here, we propose a fieldable process that includes an enrichment step to concentrate collected genetic material in a small volume. The enrichment approach uses capillary electrophoresis and an RT-qPCR-compatible buffer, which allow enrichment of the RNA by about 5-fold within only 10 minutes of operation. Our detection process consists of air sampling through electrostatic precipitation, RNA extraction via heating, RNA enrichment, and RT-qPCR for detection. We optimized each step of the process and estimated a detection sensitivity of 3106 {+/-} 2457 genome copies (gc) per m3 of air. We then performed an integration experiment and confirmed a sensitivity of 5654 gc/m3 with a detection rate of 100% and a sensitivity of 4221 gc/m3 with a detection rate of 78.6%. When using fast RT-qPCR, the latency of the whole process is down to 61 minutes. Given that our sensitivity falls in the low range of influenza and SARS-CoV-2 concentrations reported in indoor spaces, our study shows that, with enrichment, airborne pathogen detection can be made sufficiently sensitive for practical use.

Efficient and reproducible protein extraction is a critical step in mass spectrometry-based proteomics workflows, particularly for complex host-pathogen systems where low-abundance immune-associated proteins are difficult to detect. Probe sonication methods used for cell lysis requiring mitigation of excessive heat generation, to prevent degradation of biologically important proteins, while also limiting throughput and potentially introducing sample-to-sample variability. In this study, we evaluated adaptive focused acoustics (AFA) technology as an alternative approach for macrophage lysis and protein extraction and digestion within a standard proteomics workflow coupled with mass spectrometry. We observed that AFA technology reduced hands-on processing times and overall workflow timelines and single-sample AFA technology improves proteome coverage, dynamic range, and reproducibility. We also evaluated multiplexed AFA technology for lysis, and we observed an exclusive macrophage proteome and influence on replicate reproducibility and dynamic range detection for low abundant proteins. Moreover, multiplexed AFA technology for macrophage lysis and digestion further increased protein identifications, replicate reproducibility, and dynamic range. Considering the AFA-exclusive proteome, 86 proteins were detected across all AFA-based lysis and digestion methods, including low-abundance proteins associated with macrophage homeostasis, inflammatory response, and transport. Together, these findings demonstrate that AFA technology enhances reproducibility, throughput, and proteome depth for macrophage protein extraction while enabling the detection of biologically relevant low-abundance immune-associated proteins. These improvements provide a strong foundation for future investigation of host-pathogen infection models, where pathogen-derived proteins remain challenging to detect within complex host proteomes.

This study proposes a novel optical method of detecting and reducing SARS-CoV-2 transmission, the virus responsible for the COVID-19 pandemic that is sweeping the world today. SARS-CoV-2 belongs to the {beta}-coronaviruses characterized by the crown-shaped spike protein that protrudes out of the virus particles, giving the virus a "corona" shape; hence the name coronavirus. This virus is similar to the viruses that caused SARS (severe acute respiratory syndrome) and MERS (Middle East respiratory syndrome), the other two coronavirus epidemics that were recently contained within the last ten years. The technique being proposed uses a light source from a smart phone and a mobile spectrophotometer to enable detection of viral proteins in solution or paper as well as protein-protein interactions. The proof-of-concept is shown by detecting soluble preparations of spike protein subunits from SARS-CoV-2, followed by detection of the actual binding potential of the spike protein with its host receptor, the angiotensin-converting enzyme 2 (ACE2). The results are validated by showing that this method can detect antigen-antibody binding using two independent viral protein-antibody pairs. The binding could be detected optically both in solution and on a solid support such as nitrocellulose membrane. Finally, this technique is combined with DC bias to show that introduction of a current into the system can be used to disrupt the antigen-antibody reaction, suggesting that the proposed extended technique can be a potential means of not only detecting the virus, but also reducing virus transmission by disrupting virus-receptor interactions electrically. SignificanceThe measured intensity of light can reveal information about different cellular parameters under study. When light passes through a bio-composition, the intensity is associated with its content. The nuclei size, cell shape and the refractive index variation of cells contributes to light intensity. In this work, an optical label-free real time detection method incorporating the smartphone light source and a portable mini spectrometer for SARS-CoV-2 detection was developed based on the ability of its spike protein to interact with the ACE2 receptor. The light interactions with control and viral protein solutions were capable of providing a quick decision regarding whether the sample under test was positive or negative, thus enabling SARS-CoV-2 detection in a rapid manner.

The biological functions of lipids largely depend on their chemical structures. The position of C=C bonds is an essential attribute that determines the structures of unsaturated lipids. Here, we developed a new type of chemical derivatization method for C=C bond using aziridination reaction. This new cyclization method for the C=C bonds in lipids based on the direct N-Me aziridination reaction of olefins using N-methyl-O-tosylhydroxylamine (TsONHCH3) as the aminating reagent. When combined with the tandem MS analysis, this novel activation approach for C=C bonds enables the accurate identification their positions in different kinds of unsaturated lipids. Furthermore, an integrated workflow has been established for comprehensively identifying the C=C bond positional isomers of lipids in complicated biological sample. This work provided a new chemical approach for the structural lipidomics.

SIMIT-seq is a bead-free, scalable microfluidic platform designed for high-efficiency single-cell mRNA sequencing. Conventional microfluidic-based single-cell RNA sequencing platforms rely heavily on barcoded beads and intricate co-encapsulation schemes, often constrained by double Poisson limitations and the complexities of bead synthesis. In contrast, SIMIT-seq eliminates the need for beads entirely by employing a deterministic, orthogonal barcoding strategy within a two-dimensional micro-well array. This platform achieves an impressive single-cell indexing rate of 96.6% without the need for complex microfluidic operations. Here, we describe the design and fabrication of the SIMIT-seq platform, outline its workflow for transcript capture and library preparation, and demonstrate its application in profiling K562 cells. Our results validate both the high fidelity of molecular barcode immobilization and the systems capability to support downstream single-cell mRNA sequencing. SIMIT-seq offers a cost-effective and scalable alternative for single-cell transcriptomics, providing a promising foundation for future single-cell omics applications.

This new method has the capacity to dynamically analyse the metabolome of interest in diverse biological matrixes by offering coverage of rat urine, plasma, liver, brain, intestine, stomach, heart, spleen, lung, faeces, fresh plant tissues, cells and microbes. In addition, this new method enables specific and efficient analysis of microdontia metabolomes, non-microdontia and whole cell metabolomes, as well as can engage in absolute determination of 84 key clinical-wide metabolites in different biological matrixes, to enable the complementary support of clinical diagnosis and classification of diseases. To demonstrate the applicable capacity of this new method, multiple-matrixes differential metabolomes were firstly characterized using this new method to coordinate metabolic modifications underlie hepatitis induced by carbon tetrachloride (CCL4) in rats, such finding provides novel insight into the pathogenesis and therapeutics of hepatitis in clinic. Altogether, we are fully confident that this new metabolomics method will be widely welcomed by scientists in different niches to solve their key questions accordingly.

Polymerase chain reaction (PCR) is a method widely used to amplify trace amount of nucleic acids. It needs a process of thermocycling (repeated alternation of temperature). Traditional thermocycler relies on bulk size of metal block to achieve thermocycling, which results in high cost and the lack of portability. Here, a PCR chip made of graphene Transparent Conductive Films (TCFs) was employed. The thermocycling of the chip was fulfilled by a temperature programed microcontroller and a cooling fan under a low driving voltage (12V). A 35 cycles PCR was accomplished within 13 minutes using the chip and the thermocycler. The transparency of the graphene PCR chip enables the PCR reaction to be visually monitored by naked eye for a color change. The PCR chip and the thermocycler have a low cost at $2.5 and $6 respectively, and thus are feasible for Point-of-care testing (POCT) of nucleic acids in a disposable manner. The whole platform makes it possible to perform a low-cost testing of nucleic acids for varieties of purposes outside of laboratories or at resource limited locations.

Understanding proteoforms, i.e., the various molecular forms in which proteins can exist, is important for deciphering biological processes and diseases. While capillary zone electrophoresis (CZE) proved advantageous for proteoform separation, limited sample loading capabilities restrict its application. Here, we present a novel comprehensive two-dimensional nanoLCxCZE-MS platform for deep top-down proteomics (TDP). The 2D platform is highly automated, enabling robust performance and the possibility to perform proteoform quantitation as demonstrated by isobaric labeling experiments. The high orthogonality of reversed-phase LC and CZE leads to a peak capacity of 2200, leading to an increase in the number of identified proteoforms in a human Caucasian colon adenocarcinoma cell lysate sample by a factor of 3 compared to nanoLC-MS. Furthermore, CZE mobilities enable the attribution of many more proteoforms to a certain proteoform family on the MS1-level. Overall, the flexible platform enables highly efficient separation of intact proteoforms combined with sensitive MS-based TDP workflows, both for untargeted and targeted analysis of complex biological samples. Graphical AbstractWe report a robust and automated comprehensive nanoLCxCZE-MS platform for top-down proteomics. In addition to large volume sample injection and separation by hydrophobicity in the nanoLC, the orthogonal separation by CZE in the second dimension leads to a strong increase in peak capacity and, thus, in the number of identified proteoforms. CZE mobilities also enable the attribution of many more proteoforms to a proteoform family on the MS1-level. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=46 SRC="FIGDIR/small/725123v1_ufig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@df07b6org.highwire.dtl.DTLVardef@736d5corg.highwire.dtl.DTLVardef@10cef1org.highwire.dtl.DTLVardef@1825b55_HPS_FORMAT_FIGEXP M_FIG C_FIG

Although many lab-on-chip applications require inch-sized devices with microscale feature resolution, achieving this via current 3D printing methods remains challenging due to inherent tradeoffs between print resolution, design complexity, and build sizes. Inspired by microscopes that can switch objectives to achieve multiscale imaging, we report a new optical printer coined as Multipath Projection Stereolithography (MPS) specifically designed for printing microfluidic devices. MPS is designed to switch between high-resolution (1xmode, [~]10{micro}m) and low-resolution (3x mode, [~]30{micro}m) optical paths to generate centimeter sized constructs (3cm x 6cm) with a feature resolution of [~]10{micro}m. Illumination and projection systems were designed, resin formulations were optimized, and slicing software was integrated with hardware with the goal of ease of use. Using a test-case of micromixers, we show user-defined CAD models can be directly input to an automated slicing software to define printing of low-resolution features via the 3x mode with embedded microscale fins via 1x mode. A new computational model, validated using experimental results, was used to simulate various fin designs and experiments were conducted to verify simulated mixing efficiencies. New 3D out-of-plane micromixer designs were simulated and tested. To show broad applications of MPS, multi-chambered chips and microfluidic devices with microtraps were also printed. Overall, MPS can be a new fabrication tool to rapidly print a range of lab-on-chip applications.

Human saliva contains a plethora of proteins whose presence and concentration can be monitored for diagnosis and progression of disease. Saliva has been extensively probed for the diagnosis of several systemic and infectious diseases because of the ease with which it can be collected. However, amylase, the most abundant protein found in saliva can obscure the detection of low-abundance proteins by MALDI-ToF MS (matrix-assisted laser desorption/ionization-time of flight mass spectrometry) and diminish the diagnostic utility of this specimen type. In the present study, we used a device to deplete salivary amylase from water-gargle samples through affinity adsorption. After depletion, profiling of the saliva proteome was performed by MALDI-ToF MS on gargle samples from subjects whose COVID-19 (coronavirus disease 2019) status was confirmed by NP (nasopharyngeal) swab RT-qPCR (reverse transcription polymerase chain reaction). Amylase depletion led to the enhancement of signal intensities of various peaks as well as the detection of previously unobserved peaks in the MALDI-ToF spectra. The overall specificity and sensitivity after amylase depletion was 100% and 85.17% respectively for detecting COVID-19. Our simple, rapid and inexpensive technique to deplete salivary amylase can be used to unmask spectral diversity in saliva by MALDI-ToF MS, reveal low-abundant proteins and aid in the establishment of novel biomarkers for diseases.