Isoelectric focusing array with immobilized pH gradient and dynamic scanning imaging for diabetes diagnosis

Microcolumn and coelution hydration of oil seal blood spot for efficient screening of newborn α-thalassemia via chip isoelectric focusing

BACKGROUND

The global prevalence of α-thalassemia necessitates effective newborn screening strategies due to its severe clinical consequences. Traditional methods such as liquid chromatography (LC), capillary electrophoresis (CE), and isoelectric focusing (IEF) face limitations, including low separation efficiency, poor sensitivity for detecting Hb Bart's, and time-intensive operations, particularly with dried blood spots (DBS). These limitations hinder timely and accurate screening. This study addresses the need for a more efficient, sensitive, and rapid method for detecting Hb Bart's in newborns.

RESULTS

We enhanced IEF separation and sensitivity by designing a microfluidic IEF (mIEF) system with shortened columns and employing a coelution sample loading technique using oil-sealed blood spots for rapid sample pretreatment. Our experiments demonstrated significant improvements: the total analysis time was reduced from 1110 min (IPG IEF) and 46 min (LC) per batch to 36 min per batch. For individual samples, the focusing time decreased from 6 min (previous mIEF) to 3 min, with the microcolumn length shortened by 50 %, from 30 mm to 15 mm. The developed method showed excellent consistency with clinical Bart's detection and PCR diagnosis, achieving 100 % sensitivity and 98 % specificity for α-thalassemia screening.

SIGNIFICANCE AND NOVELTY

Our novel mIEF method provides an efficient, sensitive, and rapid tool for screening newborns for α-thalassemia. This advancement addresses the limitations of traditional techniques, improving early diagnosis and intervention strategies and ultimately enhancing health outcomes for at-risk newborns.

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.

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.

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.

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.

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.

[Speculation of hemoglobin A3 peak position in clinical cation exchange high performance liquid chromatogram of the diabetic blood sample with microarray isoelectric focusing]

Hemoglobin A1c (HbA1c) is a major component of glycated hemoglobin in human red blood cells. It has been proven to be a significant biomarker for the diagnosis of diabetes; its content in fresh red cells in diabetes blood reflects the average level of blood glucose over the previous three months. Thus, HbA1c level has been used for the assessment of long-term glycemic control in diabetes; the level of 6.5% HbA1c has been certified as a critical cut-off for the diabetes diagnosis. The current commonly used method for HbA1c quantification is based on cation-exchange high performance liquid chromatography (CX-HPLC). The method has advantages such as high stability, rapidity, and automation, but there are still some unidentified peaks of Hb species in CX-HPLC (VARIANT Ⅱ system); in particular, the presence of HbA3 (a glutathiolated Hb) affects the accurate determination of HbA1c. HbA3 is usually present in healthy adult blood samples at 2%-4%, but the concentration of HbA3 increases due to the protection of erythrocytes from oxidation, resulting in decreased HbA1c. However, the relative location of the HbA3 peak in the CX-HPLC clinical chromatogram has not been established. To address this issue, we extracted Hb species from fresh blood samples obtained from a hospital in an anaerobic environment to avoid possible redox reactions of Hb and glutathione. After the extraction, the Hb samples were analyzed using two methods: a low-resolution CX-HPLC (5/50 mm column) currently used for diabetes diagnosis and a high-resolution cationic exchange HPLC (Mono-S 5/50 mm column), to identify the peak corresponding to HbA3. The CX-HPLC analysis of fresh blood samples indicated that the unknown peak P3 located between HbA1c and HbA0 peaks corresponded to the HbA3 peak between HbA1c and HbA0 in the Mono-S-HPLC. Microarray isoelectric focusing (IEF) was used for the micro-preparation of HbA3, HbA1c, and HbA0 in healthy blood samples; then, the micro-prepared species of HbA3, HbA1c, and HbA0 were individually identified via Mono-S-HPLC. The results of the CX-HPLC, Mono-S-HPLC, and microarray IEF experiments indicated that the P3 peak might correspond to HbA3. To confirm this, glutathiolated Hb samples were synthesized via acetylphenylhydrazine and analyzed using both the Mono-S- and CX-HPLC systems. The results showed that the content of both glutaminated hemoglobin of HbA3 in Mono-S-HPLC and P3 in CX-HPLC increased, implying the peak of P3 with the retention time of 1.50 min in CX-HPLC was the peak corresponding to HbA3 in Mono-S-HPLC and microarray IEF. Based on the above experiments and our previous results, the influence of HbA3 on both the analysis of HbA1c in blood samples and the diabetes diagnosis needs to be considered and discussed. The study results are significant for the tentative assignment of peak P3 and for offering more information on diabetes diagnosis using CX-HPLC in the clinical setting.

Recent advances in isoelectric focusing of proteins and peptides

This review article describes the significant recent advances in Isoelectric Focusing from the period 2015-2020. The review highlights the principles and common challenges faced in Isoelectric Focusing as well as its applications. This review also details the recent advances in various modes of Isoelectric Focusing in various platforms and future directions for the technique.

A Three-Dimensional Paper-Based Isoelectric Focusing Device for Direct Analysis of Proteins in Physiological Samples

On-site protein analysis is crucial for disease diagnosis in community and family medicine in which microfluidic paper-based analytical devices (μPADs) have attracted growing attention. However, the practical applications of μPADs in protein analysis for physiological samples with high complexity is still limited. Herein, we developed a three-dimensional (3D) paper-based isoelectric focusing (IEF) platform, which is composed of power supply, reservoirs, and separation channel and made by the origami and stacking method, to simultaneously separate and enrich proteins in both low-salt and high-salt samples. Under the optimized experimental conditions, standard proteins (bovine hemoglobin (BHb) and phycocyanin (Phy)) were separated within 18 min under a 36 V power supply and obtained a 10-fold enrichment using the 3D paper-based IEF platform. Then, the capability of the 3D paper-based IEF platform for direct pretreatment of high-salt samples using a 12 V battery as power supply was measured through separating three standard proteins in saline (0.9% NaCl) with separation resolution (SR) > 1.29. Through further coupling with colorimetric and lateral flow strip measurements, the 3D paper-based IEF platform was applied to directly pretreat and quantitatively analyze microalbuminuria and C-reactive proteins in clinical urine and serum samples with analytical results with relative deviations of <8.4% and < 13.1%, respectively, to the clinical test results. This work proposes a new strategy to minimize the difficulty of directly processing high-salt samples with the traditional IEF system and provides a versatile, miniaturized, and low voltage demand analytical platform for on-site analysis of proteins in physiological samples.

Liquid-Phase Ion Trap for Ion Trapping, Transfer, and Sequential Ejection in Solutions

In this study, a new method/mechanism to manipulate ions in solution was developed, based on which liquid-phase ion trap was built. In this liquid-phase ion trap, ion manipulations conventionally performed in a quadrupole ion trap or in a trapped ion mobility spectrometer placed in a vacuum were achieved in solutions. Through theoretical derivation and numerical simulation, it is found that ions have different motional characteristics than those in vacuum. Instead of a radio frequency quadrupole electric field, tunable DC electric fields together with a constant liquid flow were applied to control ion motions in solution. Different ions could be trapped and focused in a potential well, and ion densities could be increased by over 100-fold. By adjusting the DC electric field of the potential well, trapped ions could be transferred into another trapping region or sequentially released for detection. Ions released from the liquid-phase ion trap were then detected by a mass spectrometer interfaced with an electrospray ionization source. Since the ion manipulation mechanism in solution is different and complementary to that in vacuum, the use of a liquid-phase ion trap could also boost detection sensitivity and the mixture analysis capability of a mass spectrometer.

Liquid Plasticine Integrated with Isoelectric Focusing for Miniaturized Protein Analysis

Developing miniaturized and rapid protein analytical platforms is urgently needed for on-site protein analysis, which is important for disease diagnosis and monitoring. Liquid marbles (LMs), a kind of particle-coated droplets, as ideal microreactors have been used in various fields. However, their application as analytical platforms is limited due to the difficulty of pretreating complex samples in simple LMs. Herein, inspired by the microfluidic chip, we propose a strategy through fabricating fluid channels using deformable LM, termed liquid plasticine (LP), to achieve sample pretreatment function. Through combining isoelectric focusing (IEF) with an LP channel, an LP-IEF platform with simultaneous protein separation and concentration functions is realized. The pretreatment capability of the LP-IEF system for proteins in physiological samples is proven using standard proteins and human serum with the results of a clear separation, 10-fold concentration, and a resolution of 0.03 pH. Through cutting the LP after IEF to LMs and transiting isolated LMs containing target proteins for further downstream colorimetric and mass spectrometry measurements, the quantitative analysis of clinical microalbuminuria and identification of α-1-microglobulin/bikunin precursor in clinical diabetic urine samples are achieved. This work proposes a strategy to develop LMs/LPs as a multifunctional integrated analytical platform and the miniaturized LP-IEF device as a rapid protein analytical platform.

Identification of chicken meat quality via rapid array isoelectric focusing with extraction of hemoglobin and myoglobin in meat sample

Isoelectric focusing (IEF) has been used for determination of meat quality with high stability analysis. However, it still suffered from time-consuming, laborious and cost-effective performances, e.g., 3 h protein extraction, more than 10 h rehydration time, 5-12 h focusing time, and imaging of protein band. To overcome these issues, a speedy extraction of colorful proteins was developed by controlling extraction and centrifugation of 0.2g sample within 10 min and 15 min respectively; a rapid analytical method was designed by using a quick array IEF with 25 min rehydration, 7 min focusing, 2 min online scanning and imaging of focused proteins. The total analytical time was well controlled within 1 h, significantly less than the traditional IEF time of 24 h. To demonstrate the proposed method, 18 chickens were classified into three groups, e.g., the normal slaughtering, death treatment underwater, and death with infection via the New castle disease (NDV) virus. The experiments demonstrated that two Mb bands with pI 6.8 and 7.4 were present in slaughtered chickens, while four other bands with pI 6.83, 6.95, 7.09, and 7.13 were observed in abnormal chicken. The additional four proteins bands were identified by western blot (WB) as hemoglobin proteins. Furthermore, array Immobilized pH Gradient (IPG) has high sensitivity (absolute LOD of Mb and Hb were 1.3 ng and 5.5 ng), fair stability (RSD values of 2.32%, 2.27%, and 1.69%) for slaughtered, drowned, NDV-infected chickens for intra-day and (2.94%, 1.66%, and 1.07%) for inter-days, and good recovery (100%, 98.25% and 99.75%). Finally, the developed method could be used for the identification of chicken meat quality with less time and small volume reagents consuming.