Adaptive immunity systems found in different organisms fall into two major types. Prokaryotes possess CRISPR-Cas systems that recognize former invaders using memorized (captured) pieces of their DNA ...as pathogen signatures. Mammals possess a vast repertoire of antibodies and T-cell receptor variants generated in advance. In this second type of adaptive immunity, a pathogen presentation to the immune system specifically activates the cells that express matching antibodies or receptors. These cells proliferate to fight the infection and form the immune memory. The principle of preemptive production of diverse defense proteins for future use can hypothetically take place in microbes too. We propose a hypothesis that prokaryotes employ diversity-generating retroelements to prepare defense proteins against yet-unknown invaders. In this study, we test this hypothesis with the methods of bioinformatics and identify several candidate defense systems based on diversity-generating retroelements.
The human gut microbiome plays an important role in both health and disease. Recent studies have demonstrated a strong influence of the gut microbiome composition on the efficacy of cancer ...immunotherapy. However, available studies have not yet succeeded in finding reliable and consistent metagenomic markers that are associated with the response to immunotherapy. Therefore, the reanalysis of the published data may improve our understanding of the association between the composition of the gut microbiome and the treatment response. In this study, we focused on melanoma-related metagenomic data, which are more abundant than are data from other tumor types. We analyzed the metagenomes of 680 stool samples from 7 studies that were published earlier. The taxonomic and functional biomarkers were selected after comparing the metagenomes of patients showing different treatment responses. The list of selected biomarkers was also validated on additional metagenomic data sets that were dedicated to the influence of fecal microbiota transplantation on the response to melanoma immunotherapy. According to our analysis, the resulting cross-study taxonomic biomarkers included three bacterial species: Faecalibacterium prausnitzii, Bifidobacterium adolescentis, and Eubacterium rectale. 101 groups of genes were identified to be functional biomarkers, including those potentially involved in the production of immune-stimulating molecules and metabolites. Moreover, we ranked the microbial species by the number of genes encoding functionally relevant biomarkers that they contained. Thus, we put together a list of potentially the most beneficial bacteria for immunotherapy success.
, E. rectale, and three species of bifidobacteria stood out as the most beneficial species, even though some useful functions were also present in other bacterial species.
In this study, we put together a list of potentially the most beneficial bacteria that were associated with a responsiveness to melanoma immunotherapy. Another important result of this study is the list of functional biomarkers of responsiveness to immunotherapy, which are dispersed among different bacterial species. This result possibly explains the existing irregularities between studies regarding the bacterial species that are beneficial to melanoma immunotherapy. Overall, these findings can be utilized to issue recommendations for gut microbiome correction in cancer immunotherapy, and the resulting list of biomarkers might serve as a good stepping stone for the development of a diagnostic test that is aimed at predicting patients' responses to melanoma immunotherapy.
The lack of comprehensive diagnostics and consensus analytical models for evaluating the status of a patient’s immune system has hindered a wider adoption of immunoprofiling for treatment monitoring ...and response prediction in cancer patients. To address this unmet need, we developed an immunoprofiling platform that uses multiparameter flow cytometry to characterize immune cell heterogeneity in the peripheral blood of healthy donors and patients with advanced cancers. Using unsupervised clustering, we identified five immunotypes with unique distributions of different cell types and gene expression profiles. An independent analysis of 17,800 open-source transcriptomes with the same approach corroborated these findings. Continuous immunotype-based signature scores were developed to correlate systemic immunity with patient responses to different cancer treatments, including immunotherapy, prognostically and predictively. Our approach and findings illustrate the potential utility of a simple blood test as a flexible tool for stratifying cancer patients into therapy response groups based on systemic immunoprofiling.
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•Development of a machine learning-based clinical immunoprofiling platform•Depiction of immune states by multiparameter flow cytometry and bulk RNA-seq using peripheral blood•Identification and validation of five immunotypes conserved across diverse diagnoses•Potential clinical utility in stratifying treatment responses via a simple blood test
Dyikanov et al. developed a machine learning platform that uses a simple blood test to reveal cellular compositions reflective of a person’s immune system. These compositions, delineated into five conserved immunotypes, can reflect a person’s disease status or how someone responds to specific treatments, thus underscoring their potential clinical utility for cancer patients.
Introduction Flow cytometry is widely used in clinical and research laboratories for diagnostics, biomarker discovery, and immune system monitoring. Flow cytometry data processing still uses gating- ...and clustering-based approaches that are highly time-consuming and subjective. Data processing time increases with panel size and number of detected populations, posing challenges to the search for new biomarkers. Low reproducibility and method limitations have thus far hindered efforts to automate and standardize flow cytometry data processing; hence, these efforts have not yielded any significant advancements in data processing methods. Here we present a new ML-based algorithm for automated cell-type labeling. Our supervised ML approach allows us to classify every event in a flow cytometry data file solely based on the presence and absence of markers, without the need for prior knowledge or assumption about cell population content in the sample. This approach enables the detection of rare and/or new cell populations with a high average quality metric (f1-score). The rapid and high-quality analysis our algorithm can perform renders it applicable in clinical settings, particularly for detecting hematological abnormalities and cancers. Methods We processed 500 blood samples from a cohort of healthy donors and patients with various cancer diagnoses using 10 different 18-channel multicolor flow cytometry panels. We then used data from either the entire or a portion of these 500 samples in a 3:1 split for training:test datasets to train and test our algorithm on each cytometry panel. To do this, we manually matched cells with certain cellular phenotypes to create 10 high-quality training sets for supervised learning and 10 test datasets, one pair for each of the 10 panels. To train the cell type classifier, we set up a two-level boosting-based model. The first-level model filters out outliers, including dead cells, cellular debris, beads, and other undefined particles, in order to hone in on the target population. The second-level model for predicting cell types within a target population is defined by two approaches. The population-based approach detects major subpopulation types in a target population and predicts the precise population labels. This approach is useful for labeling a small number of previously known or predicted subpopulations. The marker-based approach is useful for target populations with large numbers of subpopulations, such as T cells harboring different combinations of cell-surface receptors. It predicts the presence or absence of specific markers on each cell to assign its phenotype. It also allows us to construct complex hierarchies in order to detect new populations that are challenging to identify manually. Figure 1 outlines our workflow. Results We validated our final set of 10 trained models on our test dataset. The summarized number of detected cell populations in the test dataset was 221, which corresponds to the number of unique cell types predicted by our models. Table 1 shows the evaluation metrics for our algorithm for populations with > 0.1% whole blood cells (WBCs).The average quality metric (f1-score) for all antibody panels used is 0.86. This value is the mean of all f1-scores calculated for all cell populations identified by our algorithm. Mean f1-score is the highest (0.96) for large populations, lower (0.87) for mid-sized populations, and lowest but acceptable (0.77) for small populations. Mean quality score for the marker-based models is also high (0.96). Compared to manual evaluation that took approximately 1 hour to analyze one data file, the algorithm completed analysis within 10 seconds. Conclusion Our new algorithm automates cell labeling and produces high-quality outputs that are comparable to manual processing, but with a much shorter turnaround time (TAT) and without the need for prior knowledge or expert competence from the user. Importantly, it allows us to effectively and accurately filter out outliers, identify the target population, and divide this target population into multiple cell subtypes including new and rare cell subpopulations, all without a priori assumptions about cell population content in the sample. Given its ability to perform high-quality cell population analysis and its short TAT, our algorithm provides rapid, unbiased, and precise cell typing that will have utility for the diagnosis of heme malignancies and immunoprofiling.
Abstract
Recent advances in immunotherapy demonstrate the need to further understand the characteristics of an individual cancer patient’s immune system and how it influences responses to cancer ...treatment. Here, we developed an immunoprofiling platform to evaluate the features in the blood of cancer patients to test the hypothesis that peripheral immune cell heterogeneity could be used to stratify these patients into different categories or immunotypes to monitor disease progression and treatment response.
To that end, we established a unique diagnostic immunoprofiling assay and analytical framework based on the analysis of leukocytes in the peripheral blood using multiparameter flow cytometry. Supervised manual gating of flow cytometry data from a cohort of 50 healthy donors identified 415 cell types and immune activation states that were used to train and later independently validate machine learning models to automatically identify immune cell subsets from raw cytometry data. By applying this tool to peripheral blood samples from a mixed cohort of 299 healthy donors and 323 cancer patients, we developed a machine-learning classification model that can differentiate between these two groups with 93% accuracy. This model was further refined using spectral clustering with bootstrapping, revealing 5 clusters, or immunotypes, characterized by specific physiological immune profiles: (1) Myeloid-derived suppressor/NK cell, (2) Terminally-differentiated CD8+ T cells, (3) Mixed CD4+ T helper cells, (4) CD4+ Th1 & CD8+ T cell memory, and (5) Naive T and B lymphocytes. Interestingly, very few healthy donors could be found in clusters 1 and 2 but were assigned most frequently to cluster 5.
Matched RNA-seq was used to further validate these profiles using the cellular deconvolution algorithm, Kassandra, and differential gene expression analysis revealed immunotype-specific signatures that are consistent with immune response potential. Patients in the terminally-differentiated CD8+ T cell cluster had a narrower range of HLA-types than the other clusters, and TCR repertoire analysis indicated significantly increased clonality and reduced clonotype diversity. Within this cluster there was a high degree of overlap between TCR sequences in the peripheral blood and the tumor, indicating a relationship between peripheral blood immunotype and tumor infiltration. Altogether, the establishment of these immunotypes using peripheral blood immunoprofiling represents a promising signature that can be used to identify and stratify cancer patients that will benefit from immune-based therapies.
Citation Format: Daniiar Dyikanov, Iris Wang, Tatiana Vasileva, Polina Shpudeiko, Polina Turova, Arseniy A. Sokolov, Olga Golubeva, Evgenii Tikhonov, Anna Kamysheva, Ilya Krauz, Mary Abdou, Madison Chasse, Tori Conroy, Nicholas R. Merriam, Boris Shpak, Anastasia Radko, Anastasiia Kilina, Lira Nigmatullina, Linda Balabanian, Christopher J. Davitt, Alexander A. Ryabykh, Olga Kudryashova, Cagdas Tazearslan, Ravshan Ataullakhanov, Alexander Bagaev, Aleksandr Zaitsev, Nathan Fowler, Michael F. Goldberg. Comprehensive immunoprofiling of peripheral blood reveals five conserved immunotypes with implications for immunotherapy in cancer patients abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6664.
Two-dimensional silicon (silicene) and germanium (germanene) have attracted special attention from researchers in recent years. At the same time, highly oriented pyrolytic graphite (HOPG) and ...graphene are some of the promising substrates for growing silicene and germanene. However, to date, the processes occurring during the epitaxial growth of silicon and germanium on the surface of such substrates have been poorly studied. In this work, the epitaxial growth of silicon and germanium is studied directly during the process of the molecular beam epitaxy deposition of material onto the HOPG surface by reflection high-energy electron diffraction (RHEED). In addition, the obtained samples are studied by Raman spectroscopy and scanning electron microscopy. A wide range of deposition temperatures from 100 to 800 °C is considered and temperature intervals are determined for various growth modes of silicon and germanium on HOPG. Conditions for amorphous and polycrystalline growth are distinguished. Diffraction spots corresponding to the lattice constants of silicene and germanene are identified that may indicate the presence of areas of graphene-like 2D phases during epitaxial deposition of silicon and germanium onto the surface of highly oriented pyrolytic graphite.
Avalanche photodiodes have emerged as a promising technology with significant potential for various medical applications. This article presents an overview of the advancements and applications of ...avalanche photodiodes in the field of medical imaging. Avalanche photodiodes offer distinct advantages over traditional photodetectors, including a higher responsivity, faster response times, and superior signal-to-noise ratios. These characteristics make avalanche photodiodes particularly suitable for medical-imaging modalities that require a high detection efficiency, excellent timing resolution, and enhanced spatial resolution. This review explores the key features of avalanche photodiodes, discusses their applications in medical-imaging techniques, and highlights the challenges and future prospects in utilizing avalanche photodiodes for medical purposes. Special attention is paid to the recent progress in silicon-compatible avalanche photodiodes.
Introduction. Drying tailings are a source of gaseous chemicals, in particular radon222, which is carried by air currents along the slopes of the gorge. These processes need constant monitoring. ...Purpose of research. Establishment of climatic conditions most influencing the measurements of mountain radon and its dispersion in the atmosphere. Research methods and materials. The data of weather stations located in the Alagir mountain gorge and the data of the mathematical model of this gorge were used to establish the characteristic wind roses at the points of the gorge selected for field measurements of mountain radon. Research results. It has been established that at the nearby points of the mountain gorge, the prevailing direction and strength of the wind can differ significantly. The differences depend on the landscape features of the surface of the slopes of the mountain gorge. This factor determines the direction of migration of chemical elements and decay products of mountain radon. This conclusion is confirmed by direct measurement of wind speed at two points in the gorge, as well as by the results of mathematical modeling. Discussion of research results. A preliminary calculation of the passive impurity concentration field within the mountain gorge shows the most characteristic places on the slopes of the gorge from the point of view of measurements. Preliminary algorithm for choosing the location of measurements. Determination of 210Pb content in natural «traps» or sediments can determine the migration paths of «mountain» radon and estimate the magnitude of its interaction with the nature of the gorge, subject to the following assumptions: 1) certain sampling sites are not subject to significant seasonal disturbances; 2) natural and climatic conditions, especially the amount of precipitation, in the studied gorge should not have abnormal changes before sampling; 3) sampling should preferably be carried out in autumn on fallen leaves in hollows, where post-sedimentary migration is limited. Approbation of the proposed measurement technique is discussed. Conclusion. The prevailing winds in the side arms of mountain gorges can differ significantly from the wind rose observed in the main part of the gorge and are determined mainly by landscape features near the observation point. This must be taken into account when measuring and analyzing the direction of migration of chemicals in mountain gorges. Mathematical modeling of aerodynamic processes in mountainous regions is a proven method for studying physical processes in mountain gorges, as well as the processes of dispersion of volatile geochemical elements emitted by sources in mountain gorges. It is shown that at the nearby points of the mountain gorge, the wind roses can differ significantly, which is confirmed by both measurements and mathematical modeling. This dictates the need for observations and taking into account the local wind rose at each measurement point. The results of radon measurements are presented, confirming the effectiveness of the proposed technique. Resume. 1. The article presents the results of mathematical modeling of aerodynamic processes in mountain gorges and the spread of gaseous impurities in the air. 2. It has been established that wind roses at nearby points on the slopes of a mountain gorge can differ significantly, both in the results of the mathematical model and in the results of measurements from weather stations. It is shown that the agreement between the modeling and measurement results is satisfactory. 3. The research results can be useful in planning measurements of radon concentrations as a predictive map of the distribution of mountain radon concentrations.
The paper considers the operation of an LED lighting system with a parallel power supply from photovoltaic modules and a power grid. Such systems are supposed to be widely applicable in premises with ...limited natural lighting, particularly agrosystems where artificial light of a certain spectrum is specifically required to ensure efficient plant growth. The paper presents the scheme of the developed LED lighting system, as well as an assembled prototype containing a single 36-watt lamp. The data of the experimental study are provided on the developed LED lighting system using the developed monitoring system. The experimental study demonstrates an efficient power take-off and the reliability of the proposed scheme to competently select the characteristics and circuit solutions for the converter of voltage from the PV module. The proposed LED system allows simplification of the PV system by eliminating circuits with an inverter and storage devices, hence significantly reducing the cost of the photovoltaic systems. Likewise, such a simplicity has a positive effect on PV system reliability, which benefits the cost as well.
