Enrichment of low-abundance osteopontin in bovine milk via reciprocating free-flow isoelectric focusing.

Osteopontin (OPN) in milk plays an important role in intestinal and brain development in early infancy, and great attention has been focused on OPN isolation to add extra OPN in infant formula. However, large-scale OPN isolation is limited by the low efficiency of sample pretreatment. Herein, we utilized preparative reciprocating free-flow isoelectric focusing (RFFIEF) to showcase the enrichment of low-abundance OPN in bovine milk, which contained an extremely high concentration of unwanted proteins. The reciprocating IEF format and the design of the multi-channel collector allowed us to enrich OPN in 1 L milk within 6 h. We removed 97.5% of unwanted proteins and obtained an enrichment factor of 11. Thus, our RFFIEF method can be applied to the preparative pretreatment of the large-scale milk sample and potentially improve the efficiency of downstream OPN purification.

Marker-Free Isoelectric Focusing Patterns for Identification of Meat Samples via Deep Learning.

Isoelectric focusing (IEF) is a powerful tool for resolving complex protein samples, which generates IEF patterns consisting of multiplex analyte bands. However, the interpretation of IEF patterns requires the careful selection of isoelectric point (pI) markers for profiling the pH gradient and a trivial process of pI labeling, resulting in low IEF efficiency. Here, we for the first time proposed a marker-free IEF method for the efficient and accurate classification of IEF patterns by using a convolutional neural network (CNN) model. To verify our method, we identified 21 meat samples whose IEF patterns comprised different bands of meat hemoglobin, myoglobin, and their oxygen-binding variants but no pI marker. Thanks to the high throughput and short assay time of the microstrip IEF, we efficiently collected 1449 IEF patterns to construct the data set for model training. Despite the absence of pI markers, we experimentally introduced the severe pH gradient drift into 189 IEF patterns in the data set, thereby omitting the need for profiling the pH gradient. To enhance the model robustness, we further employed data augmentation during the model training to mimic pH gradient drift. With the advantages of simple preprocessing, a rapid inference of 50 ms, and a high accuracy of 97.1%, the CNN model outperformed the traditional algorithm for simultaneously identifying meat species and cuts of meat of 105 IEF patterns, suggesting its great potential of being combined with microstrip IEF for large-scale IEF analyses of complicated protein samples.

Gel Electrophoresis Chip Using Joule Heat Self-Dissipation, Short Run Time, and Online Dynamic Imaging.

Gel electrophoresis (GE) is one of the most general tools in biomedicine. However, it suffers from low resolution, and its mechanism has not been fully revealed yet. Herein, we presented the dispersion model of w2 (t) ∝ Tt, showing the band dispersion (w) via temperature (T) and running time (t) control. Second, we designed an efficient GE chip via the time control and rapid Joule heat self-dissipation by thermal conductive plastic (TCP) and electrode buffer. Third, we conducted the simulations on TCP and polymethylmethacrylate (PMMA) chips, unveiling that (i) the temperature of TCP was lower than the PMMA one, (ii) the temperature uniformity of TCP was better than the PMMA one, and (iii) the resolution of TCP was superior to the PMMA one. Fourth, we designed both TCP and PMMA chips for experimentally validating the dispersion model, TCP chip, and simulations. Finally, we applied the TCP chip to thalassemia and model urine protein assays. The TCP chip has merits of high resolution, rapid run of 6-10 min, and low cost. This work paves the way for greatly improving electrophoretic techniques in gel, chip, and capillary via temperature and time control for biologic study, biopharma quality control, clinical diagnosis, and so on.

Purification of low-abundance lysozyme in egg white via free-flow electrophoresis with gel-filtration chromatography.

As an effective separation tool, free-flow electrophoresis has not been used for purification of low-abundance protein in complex sample matrix. Herein, lysozyme in complex egg white matrix was chosen as the model protein for demonstrating the purification of low-content peptide via an FFE coupled with gel fitration chromatography (GFC). The crude lysozyme in egg while was first separated via free-flow zone electrophoresis (FFZE). After that, the fractions with lysozyme activity were condensed via lyophilization. Thereafter, the condensed fractions were further purified via a GFC of Sephadex G50. In all of the experiments, a special poly(acrylamide- co-acrylic acid) (P(AM-co-AA)) gel electrophoresis and a mass spectrometry were used for identification of lysozyme. The conditions of FFZE were optimized as follows: 130 µL/min sample flow rate, 4.9 mL/min background buffer of 20 mM pH 5.5 Tris-Acetic acid, 350 V, and 14 °C as well as 2 mg/mL protein content of crude sample. It was found that the purified lysozyme had the purity of 80% and high activity as compared with its crude sample with only 1.4% content and undetectable activity. The recoveries in the first and second separative steps were 65% and 82%, respectively, and the total recovery was about 53.3%. The reasons of low recovery might be induced by diffusion of lysozyme out off P(AM-co-AA) gel and co-removing of high-abundance egg ovalbumin. All these results indicated FFE could be used as alternative tool for purification of target solute with low abundance.

Diagnosis and screening of abnormal hemoglobins.

Hemoglobin (Hb) abnormalities, such as thalassemia and structural Hb variants, are among the most prevalent inherited diseases and are associated with significant mortality and morbidity worldwide. However, there were not comprehensive reviews focusing on different clinical analytical techniques, research methods and artificial intelligence (AI) used in clinical screening and research on hemoglobinopathies. Hence the review offers a comprehensive summary of recent advancements and breakthroughs in the detection of aberrant Hbs, research methods and AI uses as well as the present restrictions anddifficulties in hemoglobinopathies. Recent advances in cation exchange high performance liquid chromatography (HPLC), capillary zone electrophoresis (CZE), isoelectric focusing (IEF), flow cytometry, mass spectrometry (MS) and polymerase chain reaction (PCR) etc have allowed for the definitive detection by using advanced AIand portable point of care tests (POCT) integrating with smartphone microscopic classification, machine learning (ML) model, complete blood counts (CBC), imaging-based method, speedy immunoassay, and electrochemical-, microfluidic- and sensing-related platforms. In addition, to confirm and validate unidentified and novel Hbs, highly specialized genetic based techniques like PCR, reverse transcribed (RT)-PCR, DNA microarray, sequencing of genomic DNA, and sequencing of RT-PCR amplified globin cDNA of the gene of interest have been used. Hence, adequate utilization and improvement of available diagnostic and screening technologies are important for the control and management of hemoglobinopathies.

Smartphone-deployable and all-in-one machine vision for visual quantification analysis based on distance readout of electrophoresis titration biosensor.

As a class of point-of-care (POC) assays with visible distance readout (thermometer style), the electrophoresis titration (ET) biosensor affords high robustness, versatility, and simplicity for point-of-care quantification. However, naked-eye observation of the distance readout is unreliable in POC settings and manual processing of distance readout is time-consuming. Herein, we developed a smartphone-deployable and all-in-one machine vision for four ET biosensors (bovine serum albumin, melamine, uric acid, glutathione) to classify and quantify the samples simultaneously. To ensure accurate and rapid quantification on the smartphone, we customized the decolorization methods and edge detection operators to balance the region of interest (ROI) extraction performance and processing speed. We then established a dataset of 180 distance readout images to endow our machine vision with the ability to classify four sample types. Consequently, our machine vision demonstrated high accuracy in determining the sample type (>97.2%) and concentration (>97.3%). Moreover, expanding its applications to other targets was readily achieved by including distance readout images of other ET biosensors (e.g., hemoglobin A1c) in the dataset. Therefore, our strategy of constructing machine vision is compatible with the versatile ET biosensor technique, suggesting that the same strategy can be used for other thermometer-style POC assays.

High-resolution nucleic acid detection using online polyacrylamide gel electrophoresis platform.

Polyacrylamide gel electrophoresis (PAGE) is one of the most popular techniques for the separation and detection of nucleic acids. However, it requires a complicated detection procedure and offline detection format, which inevitably leads to band broadening and thus compromises the separation resolution. To overcome this problem, we developed an online PAGE (OPAGE) platform by integrating the gel electrophoresis apparatus with the gel imaging system, so as to obviate the need for the complicated detection procedure. Notably, OPAGE enabled the real-time monitoring of the separation process and the immediate imaging of the separation results once the electrophoresis ended. Using a series of synthetic DNAs with different lengths as samples, we demonstrated that the OPAGE platform enhanced 32-64 % of the number of theoretical plates, showed a robust dynamic range of 0.1-12.5 ng/µL, and realized a limit of detection as low as 0.08 ng/µL DNA. Based on our results, we anticipate that the OPAGE platform is a promising alternative to traditional nucleic acid gel electrophoresis for simple and high-resolution detection and quantification and nucleic acid.

Microstrip isoelectric focusing with deep learning for simultaneous screening of diabetes, anemia, and thalassemia.

BACKGROUND: Hemoglobin (Hb) is an important protein in red blood cells and a crucial diagnostic indicator of diseases, e.g., diabetes, thalassemia, and anemia. However, there is a rare report on methods for the simultaneous screening of diabetes, anemia, and thalassemia. Isoelectric focusing (IEF) is a common separative tool for the separation and analysis of Hb. However, the current analysis of IEF images is time-consuming and cannot be used for simultaneous screening. Therefore, an artificial intelligence (AI) of IEF image recognition is desirable for accurate, sensitive, and low-cost screening. RESULTS: Herein, we proposed a novel comprehensive method based on microstrip isoelectric focusing (mIEF) for detecting the relative content of Hb species. There was a good coincidence between the quantitation of Hb via a conventional automated hematology analyzer and the one via mIEF with R2 = 0.9898. Nevertheless, our results showed that the accuracy of disease diagnosis based on the quantification of Hb species alone is as low as 69.33 %, especially for the simultaneous screening of multiple diseases of diabetes, anemia, alpha-thalassemia, and beta-thalassemia. Therefore, we introduced a ResNet1D-based diagnosis model for the improvement of screening accuracy of multiple diseases. The results showed that the proposed model could achieve a high accuracy of more than 90 % and a good sensitivity of more than 96 % for each disease, indicating the overwhelming advantage of the mIEF method combined with deep learning in contrast to the pure mIEF method. SIGNIFICANCE: Overall, the presented method of mIEF with deep learning enabled, for the first time, the absolute quantitative detection of Hb, relative quantitation of Hb species, and simultaneous screening of diabetes, anemia, alpha-thalassemia, and beta-thalassemia. The AI-based diagnosis assistant system combined with mIEF, we believe, will help doctors and specialists perform fast and precise disease screening in the future.

Deep learning ResNet34 model-assisted diagnosis of sickle cell disease via microcolumn isoelectric focusing.

Traditional methods for sickle cell disease (SCD) screening can be inaccurate and misleading, and the early and accurate diagnosis of SCD is crucial for effective management and treatment. Although microcolumn isoelectric focusing (mIEF) is effective, the hemoglobinopathies must be accurately identified, wherein skilled personnel are required to analyse the bands in mIEF. Further automating and standardizing the diagnostic methods via AI to identify abnormal Hbs would be a useful endeavor. In this study, we propose a novel approach for SCD diagnosis by integrating the high throughput capability of ResNet34 in image analysis, as a deep learning convolutional neural network, for the precise separation of Hb variants using mIEF. Initially, SCD blood samples were subjected to mIEF and the resulting patterns were then captured as digital images. The sensitivity and specificity of the mIEF analysis were 100% and 97.8%, respectively, with a 99.39% accuracy. Comparison with HPLC showed a strong linear correlation (R2 = 0.9934), good agreement with the Bland-Altman plot (average difference ± 1.96 SD, bias = 9.89%) and a 100% match with the DNA analysis. Subsequently, the mIEF images were then input into the ResNet34 model, pre-trained on a large dataset, for feature extraction and classification. The integration of ResNet34 with mIEF demonstrated promising results in terms of precision (90.1%) and accuracy in distinguishing between the various SCD conditions. Overall, the proposed method offers a more effective, automated, and reduced cost approach for SCD diagnosis, which could potentially streamline diagnostic workflows and mitigate the subjectivity and variability inherent in manual assessments.

A high stable sample loading for analysis of adult alpha-thalassemia via the improved microarray isoelectric focusing of Hb species.

The isoelectric focusing has realized various improvements, including the protocols and creation of mIEF (microcolumn isoelectric focusing) instruments with excellent sensitivity for screening of diabetes and beta thalassemia. However, the problem of manual sample loading and hydration for the mIEF limits the operational capacity for stably detecting and quantitating most abnormal hemoglobin (Hb). Herein, we provided a high stable sample loading protocol for analysis of alpha thalassemia and Hb variants. In contrast to the previous volume of 20 µl, a 100 µl blood sample solution in this protocol was optimized with mixture of 6.4-7.5 and 3-10 pH carrier ampholytes, pI markers and loaded for 30 mins IPG microcolumn hydration. The hydrated microcolumn was then automatically loaded onto the mIEF chip array to which CH3COOH and NH4OH act as anodic and cathodic solutions. Lastly, the IEF was run for 9 mins. Hb H, Barts, A1c, F, A2 and CS were simultaneously separated and focused with higher resolution and sensitivity in quantifying H and Barts as low as 0.6 and 0.5 % respectively. Accordingly, there was an enhanced stability and linearity with a rapid assay time of 45 secs per sample. Moreover, analysis showed a fitting linear relationship with conventional technology at R2 = 0.9803 for H and R2 = 0.9728 for Barts thereby indicating greater accuracy confirmed by the AUC. Hence, the developed protocol could simply be employed for high stable and throughput batch sample loading of hydration, and accurate separation and quantitation of Hb variants for alpha and beta thalassemia.

Real-time and quantitative protein detection via polyacrylamide gel electrophoresis and online intrinsic fluorescence imaging.

The detection of intrinsic protein fluorescence is a powerful tool for studying proteins in their native state. Thanks to its label-free and stain-free feature, intrinsic fluorescence detection has been introduced to polyacrylamide gel electrophoresis (PAGE), a fundamental and ubiquitous protein analysis technique, to avoid the tedious detection process. However, the reported methods of intrinsic fluorescence detection were incompatible with online PAGE detection or standard slab gel. Here, we fulfilled online intrinsic fluorescence imaging (IFI) of the standard slab gel to develop a PAGE-IFI method for real-time and quantitative protein detection. To do so, we comprehensively investigated the arrangement of the deep-UV light source to obtain a large imaging area compatible with the standard slab gel, and then designed a semi-open gel electrophoresis apparatus (GEA) to scaffold the gel for the online UV irradiation and IFI with low background noise. Thus, we achieved real-time monitoring of the protein migration, which enabled us to determine the optimal endpoint of PAGE run to improve the sensitivity of IFI. Moreover, online IFI circumvented the broadening of protein bands to enhance the separation resolution. Because of the low background noise and the optimized endpoint, we showcased the quantitative detection of bovine serum albumin (BSA) with a limit of detection (LOD) of 20 ng. The standard slab gel provided a high sample loading volume that allowed us to attain a wide linear range of 0.03-10 µg. These results indicate that the PAGE-IFI method can be a promising alternative to conventional PAGE and can be widely used in molecular biology labs.

An efficient isoelectric focusing of microcolumn array chip for screening of adult Beta-Thalassemia.

Traditional capillary isoelectric focusing (cIEF), liquid chromatography (LC) and capillary zone electrophoresis (CZE) still suffered from low resolution for hemoglobinopathy screening. Herein, a 30-mm pH 5.2-7.8 microcolumn IEF (mIEF) array chip was developed for hemoglobinopathy screening. As a proof of concept, adult beta-thalassemia was chosen as a model disease. In the method, blood samples were hemolyzed via hemolysin solution and loaded into the microcolumn. The experiments showed that (i) the species of Hb A, F, A2 and variants were clearly separated in the chip, and the resolution was greatly higher than the ones of LC/CZE/cIEF; (ii) up to 24 samples could be simultaneously analyzed in 12-min run; (iii) the intraday and interday RSDs were respectively 3.32-4.91 % and 4.07-5.33 %. The assays of mIEF to total 634 samples were compared with the ones of LC (n = 327) and PCR (n = 307). The cutoff of 3.5 % HbA2 led to the sensitivity of 100 % and specificity of 89.1 % for the mIEF-based screening; and there was 96.7 % coincidence between the methods of mIEF and PCR if refer Hb A2 and F. The method had the merits of facility, efficiency, specificity and sensitivity in contrast to the currently-used methods, implying its potential to screening of beta-thalassemia and hemoglobinopathies.

Isoelectric point barcode and similarity analysis with the earth mover's distance for identification of species origin of raw meat.

In this work, by combining the microcolumn isoelectric focusing (mIEF) and similarity analysis with the earth mover's distance (EMD) metric, we proposed the concept of isoelectric point (pI) barcode for the identification of species origin of raw meat. At first, we used the mIEF to analyze 14 meat species, including 8 species of livestock and 6 species of poultry, to generate 140 electropherograms of myoglobin/hemoglobin (Mb/Hb) markers. Secondly, we binarized the electropherograms and converted them into the pI barcodes that only showed the major Mb/Hb bands for the EMD analysis. Thirdly, we efficiently developed the barcode database of 14 meat species and successfully used the EMD method to identify 9 meat products thanks to the high throughput of mIEF and the simplified format of the barcode for similarity analysis. The developed method had the merits of facility, rapidity and low cost. The developed concept and method had evident potential to the facile identification of meat species.

A handheld contactless conductivity detector for monitoring the desalting of low-volume virus and cell samples.

Desalting of biosamples is crucial for analytical techniques intolerant to abundant salts. However, there is no simple tool to monitor the desalting of low-volume biosamples so far. Here we developed a handheld capacitively coupled contactless conductivity detector (hC4D) as a miniaturized device to measure the conductivity of 75 µL biosamples. Polyether-ether-ketone (PEEK) tubing was selected as the sample reservoir for sample loading via a pipette. Another pipetting of air pushed the sample solution out of the tubing to recollect the sample. Owing to the low sample consumption and easy sample recollection, hC4D is advantageous for testing expensive biosamples, such as viruses and cells. In addition, the whole process of sample injection, conductivity measurement, recollection, and calibration of conductivity can be completed within 1 min. To verify the feasibility of hC4D, we monitored the desalting progress of gel filtration (GF) of 200 µL blood samples, ultrafiltration (UF) of 300 µL virus samples, and dialysis of 7 mL cell samples. Three rounds of GF and UF completely removed the salts but led to poor sample recovery. In contrast, low concentrations of residual salts remained and better recovery was achieved after two rounds of GF and UF. We further utilized the hC4D to monitor the dialysis and tuned the salt concentration in the cell sample, such that we maintained the viability of cells in a low conductivity environment. These results indicated that hC4D is a promising tool for optimizing the desalting procedure of low-volume biosamples.

Point of care testing (POCT) of cholesterol in blood serum via a moving reaction boundary electrophoresis titration chip.

Cholesterol (CHO) in human blood is one of the most frequently and crucially quantified substances in diagnostic laboratories. However, visual and portable point of care testing (POCT) methods have been rarely developed for the bioassay of CHO in blood samples. Here, we developed an electrophoresis titration (ET) model, a chip device of ∼60 grams, and a quantification method for the POCT of CHO in blood serum based on a moving reaction boundary (MRB). In this model, the selective enzymatic reaction is integrated with an ET chip for visual and portable quantification. At first, CHO reacted with cholesterol oxidase (CHOx) in the anode well, producing H2O2 and cholest-4-en-3-one in the solution. H2O2 further oxidized the colorless and chargeless leucocrystal violet (LCV) dye into violet colored positively charged crystal violet (CV+) and, under the influence of the electric field, the CV+ migrates in the ET channels and is titrated by the alkali of sodium hydroxide immobilized in the ET channels. The length covered by the MRB was measured as a function of the CHO content. The relevant experiments validated the feasibility of the model and method. Furthermore, the experiments revealed the high selectivity, portability, and visuality of the ET-MRB model, device, and method. Finally, the experiments showed a fair sensitivity of LOD of 5 µM, good linearity of 10-1000 µM (r2 = 0.9919), fair stability (intra-day RSD of less than 5.09% and an inter-day RSD of less than 6.36%), and high recovery (99.4-105%). All the data and results indicate the potential of the ET-MRB model, chip device, and method for POCT of CHO in human blood samples.

Reciprocating free-flow isoelectric focusing with online array ultraviolet detector for process monitoring of protein separation.

Free-flow isoelectric focusing (FFIEF) is a useful tool for separating and purifying proteins, DNA, cells, and organelles, etc. However, the online monitoring of each fraction during an FFIEF run has not been achieved yet, resulting in a lack of process monitoring of FFIEF. Herein, an online array ultraviolet (UV) detection system was developed for the easy assay of FFE fractions. The detector was integrated with an apparatus of FFIEF with 32 fractions to show the online monitoring, and bovine serum albumin (BSA) and lysozyme were chosen as the model proteins for manifesting the UV detector performance. The experiments revealed that (i) all the fluidic cells had good linearity from 0.03 to 10 mg/mL BSA and fair limits of detection (LODs) of 0.01 mg/mL; (ii) all the cells had good uniformity of UV absorbance; and (iii) the deviations of intra-day and inter-day of UV detector were respectively 3.8% and 5.8%, indicating the fair stability of the UV detector. The UV detector could be well used for the process monitoring of two model proteins through the whole FFIEF run, and the online absorbance assay of proteins at the end of FFIEF. The UV detector herein had the evident potential for rapid and convenient assay of protein fraction in FFIEF as well as other FFE modes.