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.

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.
CONCLUSIONS: 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.

This chapter provides a description of the procedure for two-dimensional electrophoresis that can be performed for any given gel size and isoelectric focusing range. This will enable the operator to recognize critical steps and gain sufficient information to generate 2D images suitable for computer-assisted analysis of 2D-gel, as well as mass spectrometry analysis for protein identification and characterization.

Fc Fusion protein represents a versatile molecular platform with considerable potential as protein therapeutics of which the charge heterogeneity should be well characterized according to regulatory guidelines. Angiotensin-converting enzyme 2 Fc fusion protein (ACE2Fc) has been investigated as a potential neutralizing agent to various coronaviruses, including the lingering SARS-CoV-2, as this coronavirus must bind to ACE2 to allow for its entry into host cells. ACE2Fc, an investigational new drug developed by Henlius (Shanghai China), has passed the Phase I clinical trial, but its huge amount of charge isoforms and complicated charge heterogeneity posed a challenge to charge variant investigation in pharmaceutical development. We employed offline free-flow isoelectric focusing (FF-IEF) fractionation, followed by detailed characterization of enriched ACE2Fc fractions, to unveil the structural origins of charge heterogeneity in ACE2Fc expressed by recombinant CHO cells. We adopted a well-tuned 3-component separation medium for ACE2Fc fractionation, the highest allowable voltage to maximize the FF-IEF separation window and a mild Protein A elution method for preservation of protein structural integrity. Through peptide mapping and other characterizations, we revealed that the intricate profiles of ACE2Fc charge heterogeneity are mainly caused by highly sialylated multi-antenna N-glycosylation. In addition, based on fraction characterization and in silico glycoprotein model analysis, we discovered that the large acidic glycans at N36, N73, and N305 of ACE2Fc were able to decrease the binding activity towards Spike (S) protein of SARS-CoV-2. Our study exemplifies the value of FF-IEF in highly complex fusion protein characterization and revealed a quantitative sialylation-activity relationship in ACE2Fc.

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.

The active ingredients from tobacco extracts were continuously separated and purified using a homemade free-flow electrophoresis apparatus. A rectangular free flow electrophoresis device was constructed for the continuous separation and preparation, and the operating conditions of the device were optimized. The fractions obtained from the free-flowing component collection unit were then detected by HPLC and GC-MS. The results showed that a 90% methanol-water solution could maximize the extraction of the active components from tobacco. Chlorogenic acid and nicotine were enriched in three and four of 24 fractions, respectively, after free-flow isoelectric focusing electrophoresis. 2-Hydroxy-2-cyclopentene-1-one, 1-(2-methyl-1,3-oxathiolan-2-yl) ethanone, nornicotine, cotinine, and scopolamine were separated and enriched synchronously. Overall, the use of free-flow electrophoresis technology for the separation and purification of the active substances in tobacco can improve the comprehensive utilization rate of tobacco.

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.

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.

Imaged capillary isoelectric focusing (icIEF) and ion-exchange chromatography (IEX) are two essential techniques that are routinely used for charge variant analysis of therapeutic monoclonal antibodies (mAbs) during their development and in quality control. These two techniques that separate mAb charge variants based on different mechanisms and IEX have been developed as front-end separation techniques for online mass spectrometry (MS) detection, which is robust for intact protein identification. Recently, an innovative, coupled icIEF-MS technology has been constructed for protein charge variant analysis in our laboratory. In this study, icIEF-MS developed and strong cation exchange (SCX)-MS were optimized for charge heterogeneity characterization of a diverse of mAbs and their results were compared based on methodological validation. It was found that icIEF-MS outperformed SCX-MS in this study by demonstrating outstanding sensitivity, low carryover effect, accurate protein identification, and higher separation resolution although SCX-MS contributed to higher analysis throughput. Ultimately, integrating our novel icIEF-HRMS analysis with the more common SCX-MS can provide a promising and comprehensive strategy for accelerating the development of complex protein therapeutics.

Because the spike (S) protein of the severe acute respiratory syndrome coronavirus (SARS-CoV) is the immunodominant antigen, the S protein and its receptor-binding domain (RBD) are both targets currently to be genetically engineered for designing the broad-spectrum vaccine. In theory, the expressed protein exists as a set of variants that are roughly the same but slightly different, which depends on the protein expression system. The variants can be phenotypically manifested as charge heterogeneity. Here, we attempted to depict the charge heterogeneity of the trimeric SARS-CoV-2 RBD by using capillary isoelectric focusing with whole-column imaging detection (cIEF-WCID). In its nature form, the electropherogram fingerprints of the trimeric RBD were presented under optimized experimental conditions. The peaks of matrix buffers can be fully distinguishable from peaks of trimeric RBD. The isoelectric point (pI) was determined to be within a range of 6.67-9.54 covering the theoretical pI of 9.02. The fingerprints of three batches of trimeric RBDs are completely the same, with the intra-batch and batch-to-batch relative standard deviations (RSDs) of both pI values and area percentage of each peak no more than 1.0%, indicating that the production process is stable and this method can be used to surveillance the batch-to-batch consistency. The fingerprint remained unchanged after incubating at 37 °C for 7 d and oxidizing by 0.015% H2O2. In addition, the fingerprint was destroyed when adjusting the pH value to higher than 10.0 but still stable when the pH was lower than 4.0. In summary, the cIEF-WCID fingerprint can be used for the identification, batch-to-batch consistency evaluation, and stability study of the trimeric SARS-CoV-2 RBD, as part of a quality control strategy during the potential vaccine production.