Ionic fluorescent sensor targeting receptor tyrosine kinases for biosystems imaging and application in flow cytometry

Proteomics mining of cancer hallmarks on a single-cell resolution

Dysregulated proteome is an essential contributor in carcinogenesis. Protein fluctuations fuel the progression of malignant transformation, such as uncontrolled proliferation, metastasis, and chemo/radiotherapy resistance, which severely impair therapeutic effectiveness and cause disease recurrence and eventually mortality among cancer patients. Cellular heterogeneity is widely observed in cancer and numerous cell subtypes have been characterized that greatly influence cancer progression. Population-averaged research may not fully reveal the heterogeneity, leading to inaccurate conclusions. Thus, deep mining of the multiplex proteome at the single-cell resolution will provide new insights into cancer biology, to develop prognostic biomarkers and treatments. Considering the recent advances in single-cell proteomics, herein we review several novel technologies with particular focus on single-cell mass spectrometry analysis, and summarize their advantages and practical applications in the diagnosis and treatment for cancer. Technological development in single-cell proteomics will bring a paradigm shift in cancer detection, intervention, and therapy.

A near-infrared GPX4 fluorescent probe for non-small cell lung cancer imaging

Glutathione peroxidase 4 (GPX4) is overexpressed in non-small cell lung cancer (H1299) cells. In this work, a near-infrared fluorescent probe ENBO-ML210 was developed. In vitro and in vivo imaging results showed that ENBO-ML210 could target and visualize GPX4 in H1299 cells, exhibiting potential for the diagnosis of non-small lung cancer.

Imaging strategies for receptor tyrosine kinase dimers in living cells

Receptor tyrosine kinases (RTKs) are the essential regulators of cell signal transduction pathways and play important roles in biological processes. RTK dimerization is generally considered the first step in receptor activation and cell communication. And the abnormal expression of RTK dimers is closely related to the occurrence and development of many diseases. Therefore, the visualization of RTK dimerization is of great significance for monitoring physiological processes. The genetic and nongenetic imaging strategies have attracted widespread attention due to their high efficiency and high sensitivity. In this review, the RTKs and their dimers as well as the advances in strategies for imaging RTK dimers are introduced. Furthermore, we analyze the limitations of existing imaging strategies and put forward suggestions for the future development of imaging probes. We expect that this review will inspire more in-depth investigation of RTK dimers, which will also broaden the application of strategies of RTK dimers in biomedical areas.

Development of constrictional microchannels and the recurrent neural network in single-cell protein analysis

Introduction: As the golden approach of single-cell analysis, fluorescent flow cytometry can estimate single-cell proteins with high throughputs, which, however, cannot translate fluorescent intensities into protein numbers. Methods: This study reported a fluorescent flow cytometry based on constrictional microchannels for quantitative measurements of single-cell fluorescent levels and the recurrent neural network for data analysis of fluorescent profiles for high-accuracy cell-type classification. Results: As a demonstration, fluorescent profiles (e.g., FITC labeled β-actin antibody, PE labeled EpCAM antibody and PerCP labeled β-tubulin antibody) of individual A549 and CAL 27 cells were firstly measured and translated into protein numbers of 0.56 ± 0.43 × 10⁴, 1.78 ± 1.0⁶ × 10⁶ and 8.11 ± 4.89 × 10⁴ of A549 cells (n_cell = 10232), and 3.47 ± 2.45 × 10⁴, 2.65 ± 1.19 × 10⁶ and 8.61 ± 5.25 × 10⁴ of CAL 27 cells (n_cell = 16376) based on the equivalent model of the constrictional microchannel. Then, the feedforward neural network was used to process these single-cell protein expressions, producing a classification accuracy of 92.0% for A549 vs. CAL 27 cells. In order to further increase the classification accuracies, as a key subtype of the recurrent neural network, the long short-term memory (LSTM) neural network was adopted to process fluorescent pulses sampled in constrictional microchannels directly, producing a classification accuracy of 95.5% for A549 vs. CAL 27 cells after optimization. Discussion: This fluorescent flow cytometry based on constrictional microchannels and recurrent neural network can function as an enabling tool of single-cell analysis and contribute to the development of quantitative cell biology.

Recent Advances in the Construction of Flexible Sensors for Biomedical Applications

The fabrication of flexible sensors is a potential way to promote the progress of modern social science and technology due to their wide applications in high-performance electronic equipment and devices. Flexible sensors based on organic materials combine the unique advantages of flexibility and low cost, increasing interest in healthcare monitoring, treatment, and human-machine interfaces. Advances in materials science and biotechnology have rapidly accelerated the development of bio-integrated multifunctional sensors and devices. Due to their excellent mechanical and electrical properties, many types of functional materials provided benefits for the construction of various sensors with improved flexibility and stretchability. In this review, recent advance in the fabrication of flexible sensors by using functional nanomaterials including nanoparticles, carbon materials, metal-organic materials, and polymers is presented. In addition, the potential biomedical applications of the fabricated flexible sensors for detecting gas molecules signals, small molecules, DNA/RNA, proteins, others are introduced and discussed.