Cell Recognition Research Articles

Implementation of an AI-based MRD Evaluation and Prediction Model for Multiple Myeloma

With the application of hematopoietic stem cell transplantation and new drugs, the progression-free survival rate and overall survival rate of multiple myeloma have been greatly improved, but it is still considered as a kind of disease that cannot be completely cured. Many patients have disease recurrence after complete remission, which is rooted in the presence of minimal residual disease MRD in patients. Studies have shown that positive MRD is an independent adverse prognostic factor affecting survival, so MRD detection is an important indicator to judge the prognosis of patients and guide clinical treatment. At present, multipa-rameter flow cytometry (MFC), polymerase chain reaction (PCR), positron emission tomography (positron emission) Several techniques, such as PET/computer tomography (CT), have been used for MRD detection of multiple myeloma. However, there is still no cure for the disease. "IFM2013-04" four clinical studies confirmed for the first time that proteasome inhibitors (PIs) and immunomodulatory drugs, The synergism and importance of the combination of IMiDs in the treatment of MM, the large Phase 3 clinical study SWOG SO777 compared the combination of bortezomib plus lenalidomide and dexamethasone. The efficacy of VRD and D established the status of VRD first-line treatment of MM, and due to the good efficacy of CD38 monoclonal antibody in large clinical studies, combination therapy with VRD has been recommended as the first-line treatment of MM. However, to explore the clinical value and problems of applying artificial intelligence bone marrow cell recognition system Morphogo in the detection of multiple myeloma minimal residual disease (MRD).

Electrochemical aptamer-based biosensing of glycan-conjugated targets

As the most complex post-translational modification, glycosylation involves the covalent linking of glycan(s) to a biomolecule. The glycosylation of proteins, peptides, lipids, and nucleic acids results in the formation of diverse glycoconjugates, like glycoproteins, peptidoglycans, glycopeptides, glycolipids, lipopolysaccharides, and glycoRNAs, and glycoconjugates are also ubiquitous on the surface of extracellular vesicles (EVs), cells, bacteria, and viruses. The conjugated glycans are integral to cell recognition (e.g., adhesion and homing), cell trafficking, immune recognition, hormone-receptor binding, protein folding and stabilization, and many other biological processes. Currently, the glycan-conjugated biological objects have found widespread applications in disease diagnosis (e.g., glycoproteins, exosomes, and circulating tumor cells) and therapy (e.g., therapeutic monoclonal antibodies). Over the past decades, a variety of methods have been established for the quantitative assay of the glycan-conjugated biological objects, in light of their significant diagnostic and therapeutic values, using antibodies, aptamers, or affinity peptides as recognition ligands. Relative to other methods, the aptamer-based electrochemical biosensors (i.e., electrochemical aptasensors) have attracted much attention in recent years in the detection of the glycan-conjugated targets, by virtue of their intrinsic advantages such as low cost, good portability, simple instrument, superior stability and anti-interference capability, and high sensitivity and selectivity. This review summarizes the biological, diagnostic, and therapeutic significance of the glycan-conjugated biological objects, and then presents the progress in the electrochemical aptasensing of the glycan-conjugated biological objects, including glycoproteins, lipopolysaccharides (LPSs), exosomes, circulating tumor cells (CTCs), and pathogenic microbes (e.g., bacteria and viruses). Moreover, the challenges and future prospects of the electrochemical aptasensing of the glycan-conjugated biological objects are discussed.

PD-1 defines a distinct, functional, tissue-adapted state in Vδ1+ T cells with implications for cancer immunotherapy

Checkpoint inhibition (CPI), particularly that targeting the inhibitory coreceptor programmed cell death protein 1 (PD-1), has transformed oncology. Although CPI can derepress cancer (neo)antigen-specific αβ T cells that ordinarily show PD-1-dependent exhaustion, it can also be efficacious against cancers evading αβ T cell recognition. In such settings, γδ T cells have been implicated, but the functional relevance of PD-1 expression by these cells is unclear. Here we demonstrate that intratumoral TRDV1 transcripts (encoding the TCRδ chain of Vδ1+ γδ T cells) predict anti-PD-1 CPI response in patients with melanoma, particularly those harboring below average neoantigens. Moreover, using a protocol yielding substantial numbers of tissue-derived Vδ1+ cells, we show that PD-1+Vδ1+ cells display a transcriptomic program similar to, but distinct from, the canonical exhaustion program of colocated PD-1+CD8+ αβ T cells. In particular, PD-1+Vδ1+ cells retained effector responses to TCR signaling that were inhibitable by PD-1 engagement and derepressed by CPI.

A narrow T cell receptor repertoire instructs thymic differentiation of MHC class Ib-restricted CD8+ regulatory T cells.

Although most CD8+ T cells are equipped to kill infected or transformed cells, a subset may regulate immune responses and preserve self-tolerance. Here, we describe a CD8 lineage that is instructed to differentiate into CD8 T regulatory cells (Tregs) by a surprisingly restricted set of T cell receptors (TCRs) that recognize MHC-E (mouse Qa-1) and several dominant self-peptides. Recognition and elimination of pathogenic target cells that express these Qa-1-self-peptide complexes selectively inhibits pathogenic antibody responses without generalized immune suppression. Immunization with synthetic agonist peptides that mobilize CD8 Tregs in vivo efficiently inhibit antigraft antibody responses and markedly prolong heart and kidney organ graft survival. Definition of TCR-dependent differentiation and target recognition by this lineage of CD8 Tregs may open the way to new therapeutic approaches to inhibit pathogenic antibody responses.

RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding.

Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC-peptide binding is vital for the development of cancer therapeutic vaccines. While recent deep learning-based methods have achieved significant performance in predicting MHC-peptide binding affinity, most of them separately encode MHC molecules and peptides as inputs, potentially overlooking critical interaction information between the two. In this work, we propose RPEMHC, a new deep learning approach based on residue-residue pair encoding to predict the binding affinity between peptides and MHC, which encode an MHC molecule and a peptide as a residue-residue pair map. We evaluate the performance of RPEMHC on various MHC-II-related datasets for MHC-peptide binding prediction, demonstrating that RPEMHC achieves better or comparable performance against other state-of-the-art baselines. Moreover, we further construct experiments on MHC-I-related datasets, and experimental results demonstrate that our method can work on both two MHC classes. These extensive validations have manifested that RPEMHC is an effective tool for studying MHC-peptide interactions and can potentially facilitate the vaccine development. The source code of the method along with trained models is freely available at https://github.com/lennylv/RPEMHC.

An ALP-responsive, anionic iridium complex for specific recognition of osteosarcoma cells.

Poor cellular permeability greatly hampers the utilization of anionic Ir(III) complexes, though efficiently emissive and remarkably stable, in cell-based diagnosis. To overcome this barrier, we present the development of an alkaline phosphatase (ALP)-responsive, anionic, and aggregation-induced emission (AIE)-active Ir(III) complex (Ir1) for specific recognition of osteosarcoma cells. Containing phosphate moieties, Ir1 exhibits a net -1 charge, enabling charge repulsion from the cell membrane and resulting in low cellular uptake and good biocompatibility in normal osteoblast cells. Upon ALP-mediated hydrolysis of phosphate groups, the resulting dephosphorylated product, Ir2, demonstrates a positive charge and increased lipophilicity, promoting cellular uptake and activating its AIE properties for specific recognition of osteosarcoma cells that express elevated levels of ALP. This study elucidates the role of ALP as an ideal trigger for enhancing the cellular permeability of phosphate ester-containing Ir(III) complexes, thus expanding the potential of anionic Ir(III) complexes for biomedical applications.

Glycosort: A Computational Solution to Post-process Quantitative Large-Scale Intact Glycopeptide Analyses.

Protein glycosylation is a post-translational modification involving the addition of carbohydrates to proteins and plays a crucial role in protein folding and various biological processes such as cell recognition, differentiation, and immune response. The vast array of natural sugars available allows the generation of plenty of unique glycan structures in proteins, adding complexity to the regulation and biological functions of glycans. The diversity is further increased by enzymatic site preferences and stereochemical conjugation, leading to an immense amount of different glycan structures. Understanding glycosylation heterogeneity is vital for unraveling the impact of glycans on different biological functions. Evaluating site occupancies and structural heterogeneity aids in comprehending glycan-related alterations in biological processes. Several software tools are available for large-scale glycoproteomics studies; however, integrating identification and quantitative data to assess heterogeneity complexity often requires extensive manual data processing. To address this challenge, we present a python script that automates the integration of Byonic and MaxQuant outputs for glycoproteomic data analysis. The script enables the calculation of site occupancy percentages by glycans and facilitates the comparison of glycan structures and site occupancies between two groups. This automated tool offers researchers a means to organize and interpret their high-throughput quantitative glycoproteomic data effectively.

Recent advances in nanopore-based analysis for carbohydrates and glycoconjugates.

Saccharides are not only the basic constituents and nutrients of living organisms, but also participate in various life activities, and play important roles in cell recognition, immune regulation, development, cancer, etc. The analysis of carbohydrates and glycoconjugates is a necessary means to study their transformations and physiological roles in living organisms. Existing detection techniques can hardly meet the requirements for the analysis of carbohydrates and glycoconjugates in complex matrices as they are expensive, involve complex derivatization, and are time-consuming. Nanopore sensing technology, which is amplification-free and label-free, and is a high-throughput process, provides a new solution for the identification and sequencing of carbohydrates and glycoconjugates. This review highlights recent advances in novel nanopore-based single-molecule sensing technologies for the detection of carbohydrates and glycoconjugates and discusses the advantages and challenges of nanopore sensing technologies. Finally, current issues and future perspectives are discussed with the aim of improving the performance of nanopores in complex media diagnostic applications, as well as providing a new direction for the quantification of glycan chains and the study of glycan chain properties and functions.

Precision in cancer diagnostics: ultra-sensitive detection of MCF-7 breast cancer cells by gold nanostructure-enhanced electrochemical biosensing.

Timely identification of cancers is pivotal in optimizing treatment efficacy and reducing their widespread impact. This study introduces a novel biosensor for the sensitive electrochemical detection of cancer cells overexpressing mucin 1 (MUC1), a well-established model for breast cancer. The sensor substrate comprises gold columnar nanostructures obtained through glancing angle deposition (GLAD) of copper nanostructures, subsequently replaced by gold via a facile galvanic replacement process. Functionalizing these gold nanostructures with aptamers targeting the MUC1 glycoproteins, a prominent cancer biomarker, enables specific recognition of MCF-7 breast cancer cells. The proposed electrochemical sensing platform offers several advantages, including high selectivity, a wide linear range of detection, a low detection limit of 30 cells per mL, and long-term stability, rendering this sensor highly desirable for definitive breast cancer diagnosis.

Overcoming challenges in cancer treatment: Nano-enabled photodynamic therapy as a viable solution.

Cancer presents a formidable challenge, necessitating innovative therapies that maximize effectiveness while minimizing harm to healthy tissues. Nanotechnology has emerged as a transformative force in cancer treatment, particularly through nano-enabled photodynamic therapy (NE-PDT), which leverages precise and targeted interventions. NE-PDT capitalizes on photosensitizers activated by light to generate reactive oxygen species (ROS) that initiate apoptotic pathways in cancer cells. Nanoparticle enhancements optimize this process, improving drug delivery, selectivity, and ROS production within tumors. This review dissects NE-PDT's mechanistic framework, showcasing its potential to harness apoptosis as a potent tool in cancer therapy. Furthermore, the review explores the synergy between NE-PDT and complementary treatments like chemotherapy, immunotherapy, and targeted therapies, highlighting the potential to amplify apoptotic responses, enhance immune recognition of cancer cells, and inhibit resistance mechanisms. Preclinical and clinical advancements in NE-PDT demonstrate its efficacy across various cancer types. Challenges in translating NE-PDT into clinical practice are also addressed, emphasizing the need for optimizing nanoparticle design, refining dosimetry, and ensuring long-term safety. Ultimately, NE-PDT represents a promising approach in cancer therapy, utilizing the intricate mechanisms of apoptosis to address therapeutic hurdles. The review underscores the importance of understanding the interplay between nanoparticles, ROS generation, and apoptotic pathways, contributing to a deeper comprehension of cancer biology and novel therapeutic strategies. As interdisciplinary collaborations continue to thrive, NE-PDT offers hope for effective and targeted cancer interventions, where apoptosis manipulation becomes central to conquering cancer. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

Water solubility and folate receptor affinity-driven plasma membrane-targeted carbon dots for cancer cell imaging.

Long-term labeling of the plasma membrane is crucial for visualizing membrane protein expression and morphological changes but is challenging due to the high fluidity of the plasma membrane, which can lead to probe diffusion or internalization of cells. Here, we precisely control the localization of carbon dots (M-CDs) on the plasma membrane without internalization after long-term observation under fluorescence microscopy. Adjusting the molar ratio of folic acid to o-phenylenediamine allowed fine-tuning of the water solubility and fluorescence emission of the carbon dots. Notably, carbon dots synthesized with a folic acid to o-phenylenediamine molar ratio of 1 : 10 (referred to as M-CD) exhibit excellent cell membrane targeting, likely due to the combination of suitable water-solubility and FA-FR affinity. The photostability of M-CDs is significantly superior to that of the commercial CellMask Crimson, allowing for specific recognition of folic acid receptor-positive cancer cells and minimal internalization over a period of up to 9 hours. This photostable, membrane-targeting M-CD provides a powerful tool for accurately, real-time, and non-invasively assessing the expression of folic acid receptors on cancer cell membranes and tumor metastasis.

Fabrication and properties of temperature-responsive imprinted sensors based on fluorescently labeled yeast cells via MVL ATRP.

Temperature-responsive yeast cell-imprinted sensors (CIPs/AuNPs/Ti3C2Tx/AuNPs/Au) were prepared based on fluorescein isothiocyanate labeled yeast cells (FITC-yeast) via metal-free visible-light-induced atom transfer radical polymerization (MVL ATRP). Here, N-isopropyl acrylamide (NIPAM) was used as a temperature-responsive functional monomer, α-methacrylic acid (MAA) was chosen as an auxiliary functional monomer, N,N'-methylene bisacrylamide (MBA) was used as a cross-linker, and FITC-yeast was selected as both a template and photocatalyst. Under the optimal conditions, the detection range of the yeast cell-imprinted sensor toward yeast cells was 1.0 × 102 to 1.0 × 109 cells per mL, and the detection limit was 11 cells per mL (S/N = 3), with a linear equation of ΔI (μA) = 8.44 log[C (cells per mL)] + 7.62 (R2 = 0.993). The sensor showed good selective recognition in the presence of interfering substances such as autolyzed yeast cells (AY), dead yeast cells (DY), human mammary epithelial cells (MCF-10A), human breast cancer cells (MCF-7) and Escherichia coli (EC). The sensor also had good consistency and reproducibility. Finally, spiked recovery experiments were performed to investigate the recognition of yeast cells in the actual sample using the yeast cell-imprinted sensor. The spiked recoveries were all in the range of 98.5-108.0%, and the RSD values were all less than 4%, indicating that the sensor had good application prospects.

Epithelial recognition and elimination against aberrant cells.

Epithelial cells, which are non-immune cells, not only function as a physical defence barrier but also continuously monitor and eliminate aberrant epithelial cells in their vicinity. In other words, it has become evident that epithelial cells possess immune cell-like functions. In fact, recent research has revealed that epithelial cells recognise the Major Histocompatibility Complex I (MHC-I) of aberrant cells as a mechanism for surveillance. This cellular defence mechanism of epithelial cells probably detects aberrant cells more promptly than the conventional immune response, making it a novel and primary biological defence. Furthermore, there is the potential for this new immune-like biological defence mechanism to establish innovative treatment for disease prevention, leading to increasing anticipation for its future medical applications. In this review, we aim to summarise the recognition and attack mechanisms of aberrant cells by epithelial cells in mammals, with a particular focus on the field of cancer. Additionally, we discuss the potential therapeutic applications of epithelial cell-based defence against cancer, including novel prophylactic treatment methods based on molecular mechanisms.

Recent advancements in machine learning for bone marrow cell morphology analysis.

As machine learning progresses, techniques such as neural networks, decision trees, and support vector machines are being increasingly applied in the medical domain, especially for tasks involving large datasets, such as cell detection, recognition, classification, and visualization. Within the domain of bone marrow cell morphology analysis, deep learning offers substantial benefits due to its robustness, ability for automatic feature learning, and strong image characterization capabilities. Deep neural networks are a machine learning paradigm specifically tailored for image processing applications. Artificial intelligence serves as a potent tool in supporting the diagnostic process of clinical bone marrow cell morphology. Despite the potential of artificial intelligence to augment clinical diagnostics in this domain, manual analysis of bone marrow cell morphology remains the gold standard and an indispensable tool for identifying, diagnosing, and assessing the efficacy of hematologic disorders. However, the traditional manual approach is not without limitations and shortcomings, necessitating, the exploration of automated solutions for examining and analyzing bone marrow cytomorphology. This review provides a multidimensional account of six bone marrow cell morphology processes: automated bone marrow cell morphology detection, automated bone marrow cell morphology segmentation, automated bone marrow cell morphology identification, automated bone marrow cell morphology classification, automated bone marrow cell morphology enumeration, and automated bone marrow cell morphology diagnosis. Highlighting the attractiveness and potential of machine learning systems based on bone marrow cell morphology, the review synthesizes current research and recent advances in the application of machine learning in this field. The objective of this review is to offer recommendations to hematologists for selecting the most suitable machine learning algorithms to automate bone marrow cell morphology examinations, enabling swift and precise analysis of bone marrow cytopathic trends for early disease identification and diagnosis. Furthermore, the review endeavors to delineate potential future research avenues for machine learning-based applications in bone marrow cell morphology analysis.

Master regulator: p53's pivotal role in steering NK-cell tumor patrol.

The p53 protein, encoded by TP53, is a tumor suppressor that plays a critical role in regulating apoptosis, cell cycle regulation, and angiogenesis in tumor cells via controlling various downstream signals. Natural killer (NK) cell-mediated immune surveillance is a vital self-defense mechanism against cancer and other diseases, with NK cell activity regulated by various mechanisms. Among these, p53 plays a significant role in immune regulation by maintaining the homeostasis and functionality of NK cells. It enhances the transcriptional activity of NK cell-activating ligands and downregulates inhibitory ligands to boost NK cell activation and tumor-killing efficacy. Additionally, p53 influences NK cell cytotoxicity by promoting apoptosis, autophagy, and ferroptosis in different tumor cells. p53 is involved in the regulation of NK cell activity and effector functions through multiple pathways. p53 also plays a pivotal role in the tumor microenvironment (TME), regulating the activity of NK cells. NK cells are critical components of the TME and are capable of directly killing tumor cells. And p53 mutates in numerous cancers, with the most common alteration being a missense mutation. These mutations are commonly associated with poor survival rates in patients with cancer. This review details p53's role in NK cell tumor immunosurveillance, summarizing how p53 enhances NK cell recognition and tumor destruction. We also explore the potential applications of p53 in tumor immunotherapy, discussing strategies for modulating p53 to enhance NK cell function and improve the efficacy of tumor immunotherapy, along with the associated challenges. Understanding the interaction between p53 and NK cells within the TME is crucial for advancing NK cell-based immunotherapy and developing p53-related novel therapeutics.

Verticillins: fungal epipolythiodioxopiperazine alkaloids with chemotherapeutic potential.

Covering: 1970 through June of 2023Verticillins are epipolythiodioxopiperazine (ETP) alkaloids, many of which possess potent, nanomolar-level cytotoxicity against a variety of cancer cell lines. Over the last decade, their in vivo activity and mode of action have been explored in detail. Notably, recent studies have indicated that these compounds may be selective inhibitors of histone methyltransferases (HMTases) that alter the epigenome and modify targets that play a crucial role in apoptosis, altering immune cell recognition, and generating reactive oxygen species. Verticillin A (1) was the first of 27 analogues reported from fungal cultures since 1970. Subsequent genome sequencing identified the biosynthetic gene cluster responsible for producing verticillins, allowing a putative pathway to be proposed. Further, molecular sequencing played a pivotal role in clarifying the taxonomic characterization of verticillin-producing fungi, suggesting that most producing strains belong to the genus Clonostachys (i.e., Bionectria), Bionectriaceae. Recent studies have explored the total synthesis of these molecules and the generation of analogues via both semisynthetic and precursor-directed biosynthetic approaches. In addition, nanoparticles have been used to deliver these molecules, which, like many natural products, possess challenging solubility profiles. This review summarizes over 50 years of chemical and biological research on this class of fungal metabolites and offers insights and suggestions on future opportunities to push these compounds into pre-clinical and clinical development.

An automatic classification method of testicular histopathology based on SC-YOLO framework.

The pathological diagnosis and treatment of azoospermia depend on precise identification of spermatogenic cells. Traditional methods are time-consuming and highly subjective due to complexity of Johnsen score, posing challenges for accurately diagnosing azoospermia. Here, we introduce a novel SC-YOLO framework for automating the classification of spermatogenic cells that integrates S3Ghost module, CoordAtt module and DCNv2 module, effectively capturing texture and shape features of spermatogenic cells while reducing model parameters. Furthermore, we propose a simplified Johnsen score criteria to expedite the diagnostic process. Our SC-YOLO framework presents the higher efficiency and accuracy of deep learning technology in spermatogenic cell recognition. Future research endeavors will focus on optimizing the model's performance and exploring its potential for clinical applications.

Cell recognition based on features extracted by AFM and parameter optimization classifiers.

Intelligent technology can assist in the diagnosis and treatment of disease, which would pave the way towards precision medicine in the coming decade. As a key focus of medical research, the diagnosis and prognosis of cancer play an important role in the future survival of patients. In this work, a diagnostic method based on nano-resolution imaging was proposed to meet the demand for precise detection methods in medicine and scientific research. The cell images scanned by AFM were recognized by cell feature engineering and machine learning classifiers. A feature ranking method based on the importance of features to responses was used to screen features closely related to categorization and optimization of feature combinations, which helps to understand the feature differences between cell types at the micro level. The results showed that the Bayesian optimized back propagation neural network has accuracy rates of 90.37% and 92.68% on two cell datasets (HL-7702 & SMMC-7721 and GES-1 & SGC-7901), respectively. This provides an automatic analysis method for identifying cancer cells or abnormal cells, which can help to reduce the burden of medical or scientific research, decrease misjudgment and promote precise medical care for the whole society.

Breast Cancer Cell Recognition System Based on Microscopic Image

Breast Cancer Cell Recognition System Based on Microscopic Image

Composition, functions, and applications of exosomal membrane proteins.

Exosomes play a crucial role in various biological processes, such as human development, immune responses, and disease occurrence. The membrane proteins on exosomes are pivotal factors for their biological functionality. Currently, numerous membrane proteins have been identified on exosome membranes, participating in intercellular communication, mediating target cell recognition, and regulating immune processes. Furthermore, membrane proteins from exosomes derived from cancer cells can serve as relevant biomarkers for early cancer diagnosis. This article provides a comprehensive review of the composition of exosome membrane proteins and their diverse functions in the organism's biological processes. Through in-depth exploration of exosome membrane proteins, it is expected to offer essential foundations for the future development of novel biomedical diagnostics and therapies.