Isolation of tumour-reactive lymphocytes from peripheral blood via microfluidic immunomagnetic cell sorting

Phenotypic targeting using magnetic nanoparticles for rapid characterization of cellular proliferation regulators

Genome-wide CRISPR screens have provided a systematic way to identify essential genetic regulators of a phenotype of interest with single-cell resolution. However, most screens use live/dead readout of viability to identify factors of interest. Here, we describe an approach that converts cell proliferation into the degree of magnetization, enabling downstream microfluidic magnetic sorting to be performed. We performed a head-to-head comparison and verified that the magnetic workflow can identify the same hits from a traditional screen while reducing the screening period from 4 weeks to 1 week. Taking advantage of parallelization and performance, we screened multiple mesenchymal cancer cell lines for their dependency on cell proliferation. We found and validated pan- and cell-specific potential therapeutic targets. The method presented provides a nanoparticle-enabled approach means to increase the breadth of data collected in CRISPR screens, enabling the rapid discovery of drug targets for treatment.

Swarm Autonomy: From Agent Functionalization to Machine Intelligence

Swarm behaviors are common in nature, where individual organisms collaborate via perception, communication, and adaptation. Emulating these dynamics, large groups of active agents can self-organize through localized interactions, giving rise to complex swarm behaviors, which exhibit potential for applications across various domains. This review presents a comprehensive summary and perspective of synthetic swarms, to bridge the gap between the microscale individual agents and potential applications of synthetic swarms. It is begun by examining active agents, the fundamental units of synthetic swarms, to understand the origins of their motility and functionality in the presence of external stimuli. Then inter-agent communications and agent-environment communications that contribute to the swarm generation are summarized. Furthermore, the swarm behaviors reported to date and the emergence of machine intelligence within these behaviors are reviewed. Eventually, the applications enabled by distinct synthetic swarms are summarized. By discussing the emergent machine intelligence in swarm behaviors, insights are offered into the design and deployment of autonomous synthetic swarms for real-world applications.

Circulating T cells: a promising biomarker of anti-PD-(L)1 therapy

Anti-PD-(L)1 therapy has shown great efficacy in some patients with cancer. However, a significant proportion of patients with cancer do not respond to it. Another unmet clinical need for anti-PD-(L)1 therapy is the dynamic monitoring of treatment effects. Therefore, identifying biomarkers that can stratify potential responders before PD-(L)1 treatment and timely monitoring of the efficacy of PD-(L)1 treatment are crucial in the clinical setting. The identification of biomarkers by liquid biopsy has attracted considerable attention. Among the identified biomarkers, circulating T cells are one of the most promising because of their indispensable contribution to anti-PD-(L)1 therapy. The present review aimed to thoroughly explore the potential of circulating T cells as biomarkers of anti-PD-(L)1 therapy and its advantages and limitations.

Merkel cell polyomavirus-specific and CD39+CLA+ CD8 T cells as blood-based predictive biomarkers for PD-1 blockade in Merkel cell carcinoma

Merkel cell carcinoma is a skin cancer often driven by Merkel cell polyomavirus (MCPyV) with high rates of response to anti-PD-1 therapy despite low mutational burden. MCPyV-specific CD8 T cells are implicated in anti-PD-1-associated immune responses and provide a means to directly study tumor-specific T cell responses to treatment. Using mass cytometry and combinatorial tetramer staining, we find that baseline frequencies of blood MCPyV-specific cells correlated with response and survival. Frequencies of these cells decrease markedly during response to therapy. Phenotypes of MCPyV-specific CD8 T cells have distinct expression patterns of CD39, cutaneous lymphocyte-associated antigen (CLA), and CD103. Correspondingly, overall bulk CD39+CLA+ CD8 T cell frequencies in blood correlate with MCPyV-specific cell frequencies and similarly predicted favorable clinical outcomes. Conversely, frequencies of CD39+CD103+ CD8 T cells are associated with tumor burden and worse outcomes. These cell subsets can be useful as biomarkers and to isolate blood-derived tumor-specific T cells.

Engineering Challenges and Opportunities in Autologous Cellular Cancer Immunotherapy

The use of a patient's own immune or tumor cells, manipulated ex vivo, enables Ag- or patient-specific immunotherapy. Despite some clinical successes, there remain significant barriers to efficacy, broad patient population applicability, and safety. Immunotherapies that target specific tumor Ags, such as chimeric Ag receptor T cells and some dendritic cell vaccines, can mount robust immune responses against immunodominant Ags, but evolving tumor heterogeneity and antigenic downregulation can drive resistance. In contrast, whole tumor cell vaccines and tumor lysate-loaded dendritic cell vaccines target the patient's unique tumor antigenic repertoire without prior neoantigen selection; however, efficacy can be weak when lower-affinity clones dominate the T cell pool. Chimeric Ag receptor T cell and tumor-infiltrating lymphocyte therapies additionally face challenges related to genetic modification, T cell exhaustion, and immunotoxicity. In this review, we highlight some engineering approaches and opportunities to these challenges among four classes of autologous cell therapies.

Apta FastZ: An Algorithm for the Rapid Identification of Aptamers with Defined Binding Affinities

Real-time biomolecular monitoring requires biosensors based on affinity reagents, such as aptamers, with moderate to low affinities for the best binding dynamics and signal gain. We recently reported Pro-SELEX, an approach that utilizes parallelized SELEX and high-content bioinformatics for the selection of aptamers with predefined binding affinities. The Pro-SELEX pipeline relies on an algorithm, termed AptaZ, that can predict the binding affinities of selected aptamers. The original AptaZ algorithm is computationally complex and slows the overall throughput of Pro-SELEX. Here, we present Apta FastZ, a rapid equivalent of AptaZ. The Apta FastZ algorithm considers the spare nature of the sequences from SELEX and is coded to avoid unnecessary comparison between sequences. As a result, Apta FastZ achieved a 10 to 40-fold faster computing speed compared to the original AptaZ algorithm while maintaining identical outcomes, allowing the bioinformatics to be completed within 1-10 h for large-scale data sets. We further validated the affinity of myeloperoxidase aptamers predicted by Apta FastZ by experiments and observed a high level of linear correlation between predicted scores and measured affinities. Taken together, the implementation of Apta FastZ could greatly accelerate the current Pro-SELEX workflow, allowing customized aptamers to be discovered within 3 days using preselected DNA libraries.

Artificial Intelligence in Regenerative Medicine: Applications and Implications

The field of regenerative medicine is constantly advancing and aims to repair, regenerate, or substitute impaired or unhealthy tissues and organs using cutting-edge approaches such as stem cell-based therapies, gene therapy, and tissue engineering. Nevertheless, incorporating artificial intelligence (AI) technologies has opened new doors for research in this field. AI refers to the ability of machines to perform tasks that typically require human intelligence in ways such as learning the patterns in the data and applying that to the new data without being explicitly programmed. AI has the potential to improve and accelerate various aspects of regenerative medicine research and development, particularly, although not exclusively, when complex patterns are involved. This review paper provides an overview of AI in the context of regenerative medicine, discusses its potential applications with a focus on personalized medicine, and highlights the challenges and opportunities in this field.

A magneto-activated nanoscale cytometry platform for molecular profiling of small extracellular vesicles

Exosomal PD-L1 (exoPD-L1) has recently received significant attention as a biomarker predicting immunotherapeutic responses involving the PD1/PD-L1 pathway. However, current technologies for exosomal analysis rely primarily on bulk measurements that do not consider the heterogeneity found within exosomal subpopulations. Here, we present a nanoscale cytometry platform NanoEPIC, enabling phenotypic sorting and exoPD-L1 profiling from blood plasma. We highlight the efficacy of NanoEPIC in monitoring anti-PD-1 immunotherapy through the interrogation of exoPD-L1. NanoEPIC generates signature exoPD-L1 patterns in responders and non-responders. In mice treated with PD1-targeted immunotherapy, exoPD-L1 is correlated with tumor growth, PD-L1 burden in tumors, and the immune suppression of CD8+ tumor-infiltrating lymphocytes. Small extracellular vesicles (sEVs) with different PD-L1 expression levels display distinctive inhibitory effects on CD8 + T cells. NanoEPIC offers robust, high-throughput profiling of exosomal markers, enabling sEV subpopulation analysis. This platform holds the potential for enhanced cancer screening, personalized treatment, and therapeutic response monitoring.

Assessing Tumorigenicity in Stem Cell-Derived Therapeutic Products: A Critical Step in Safeguarding Regenerative Medicine

Stem cells hold promise in regenerative medicine due to their ability to proliferate and differentiate into various cell types. However, their self-renewal and multipotency also raise concerns about their tumorigenicity during and post-therapy. Indeed, multiple studies have reported the presence of stem cell-derived tumors in animal models and clinical administrations. Therefore, the assessment of tumorigenicity is crucial in evaluating the safety of stem cell-derived therapeutic products. Ideally, the assessment needs to be performed rapidly, sensitively, cost-effectively, and scalable. This article reviews various approaches for assessing tumorigenicity, including animal models, soft agar culture, PCR, flow cytometry, and microfluidics. Each method has its advantages and limitations. The selection of the assay depends on the specific needs of the study and the stage of development of the stem cell-derived therapeutic product. Combining multiple assays may provide a more comprehensive evaluation of tumorigenicity. Future developments should focus on the optimization and standardization of microfluidics-based methods, as well as the integration of multiple assays into a single platform for efficient and comprehensive evaluation of tumorigenicity.