Abstract: Single-cell dispensing for automated cell isolation of individual cells has gained increased attention in the biopharmaceutical industry, mainly for production of clonal cell lines. Here, machine learning for classification of cell images is applied for 'real-time' cell viability sorting on a single-cell printer. We show that an extremely shallow convolutional neural network (CNN) for classification of low-complexity cell images outperforms more complex architectures. Datasets with hundreds of cell images from four different samples were used for training and validation of the CNNs. The clone recovery, i.e. the fraction of single-cells that grow to clonal colonies, is predicted to increase for all the samples investigated. Finally, a trained CNN was deployed on a c.sight single-cell printer for 'real-time' sorting of a CHO-K1 cells. On a sample with artificially damaged cells the clone recovery could be increased from 27% to 73%, thereby resulting in a significantly faster and more efficient cloning. Depending on the classification threshold, the frequency at which viable cells are dispensed could be increased by up to 65%. This technology for image-based cell sorting is highly versatile and can be expected to enable cell sorting by computer vision with respect to different criteria in the future

Cells are the most fundamental building block of all living organisms. The investigation of any type of disease mechanism and its progression still remains challenging due to cellular heterogeneity characteristics and physiological state of cells in a given population. The bulk measurement of millions of cells together can provide some general information on cells, but it cannot evolve the cellular heterogeneity and molecular dynamics in a certain cell population. Compared to this bulk or the average measurement of a large number of cells together, single-cell analysis can provide detailed information on each cell, which could assist in developing an understanding of the specific biological context of cells, such as tumor progression or issues around stem cells. Single-cell omics can provide valuable information about functional mutation and a copy number of variations of cells. Information from single-cell investigations can help to produce a better understanding of intracellular interactions and environmental responses of cellular organelles, which can be beneficial for therapeutics development and diagnostics purposes. This Special Issue is inviting articles related to single-cell analysis and its advantages, limitations, and future prospects regarding health benefits.

The book is a comprehensive guide that explores the use of artificial intelligence and machine learning in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs. The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine. This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals. This book is a testament to the profound impact these technologies have had and will continue to have on the pharmaceutical industry, healthcare, and ultimately, patient well-being. The editors of this volume have assembled a distinguished group of experts, researchers, and thought leaders from both the AI, ML, and pharmaceutical domains. Their collective knowledge and insights illuminate the multifaceted landscape of AI and ML in drug design and development, offering a roadmap for navigating its complexities and harnessing its potential. In each section, readers will find a rich tapestry of knowledge, case studies, and expert opinions, providing a 360-degree view of AI and ML’s role in drug design and development. Whether you are a researcher, scientist, industry professional, policymaker, or simply curious about the future of medicine, this book offers 19 state-of-the-art chapters providing valuable insights and a compass to navigate the exciting journey ahead. Audience The book is a valuable resource for a wide range of professionals in the pharmaceutical and allied industries including researchers, scientists, engineers, and laboratory workers in the field of drug discovery and development, who want to learn about the latest techniques in machine learning and AI, as well as information technology professionals who are interested in the application of machine learning and artificial intelligence in drug development.

Background: Cells from our immune system detect and kill pathogens to protect our body against many diseases. However, current methods for determining cell types have some major limitations, such as being time-consuming and with low throughput rate, etc. These problems stack up and hinder the deep exploration of cellular heterogeneity. Immune cells that are associated with cancer tissues play a critical role in revealing the stages of tumor development. Identifying the immune composition within tumor microenvironments in a timely manner will be helpful to improve clinical prognosis and therapeutic management for cancer. Single-cell RNA sequencing (scRNA-seq), an RNA sequencing (RNA-seq) technique that focuses on a single cell level, has provided us with the ability to conduct cell type classification. Although unsupervised clustering approaches are the major methods for analyzing scRNA-seq datasets, their results vary among studies with different input parameters and sizes. However, in supervised machine learning methods, information loss and low prediction accuracy are the key limitations. Methods and Results: Genes in the human genome align to chromosomes in a particular order. Hence, we hypothesize incorporating this information into our model will potentially improve the cell type classification performance. In order to utilize gene positional information, we introduce chromosome-based neural network, namely ChrNet, a novel chromosome-specific re-trainable supervised learning method based on a one-dimensional convolutional neural network (1D-CNN). The model's performance was evaluated and compared with other supervised learning architectures. Overall, the ChrNet showed highest performance among the 3 models we benchmarked. In addition, we demonstrated the advantages of our new model over unsupervised clustering approaches using gene expression profiles from healthy, and tumor infiltrating immune cells. The codes for our model are packed into a Python package publicly available online on Github. Conclusions: We established an innovative chromosome-based 1D-CNN architecture to extract scRNA-seq expression information for immune cell type classification. It is expected that this model can become a reference architecture for future cell type classification methods.

This book provides a brief overview of single-cell analysis using recent advanced technologies. The different sections cover different aspect of single cell analysis and applications with their advantages, limitations, and future challenges. The book has covered how different physical energies such as optical, electrical, and mechanical energy have been applied for single cell therapy and analysis. The recent advanced micro/nanofluidic devices have been employed for single-cell counting, manipulation, cultivation, separation, isolation, lysis, printing and patterning and host-viral interaction at single-cell level. Various chemical approaches for single-cell analysis have been discussed, such as liposome mediated materials transfer at single-cell and their analysis, discovery of antibody via single-cell, high-throughput screening of antigen-specific antibody-secreting cells, and biomolecular secretion analysis of individual cells. Moreover, different single-cell omics such as genomics, proteomics and transcriptomics have been discussed using microfluidic technologies as well as conventional approaches. The role of single cell analysis in system biology and biocatalysis have been discussed in detail. The book describes single-cell phenotyping of heterogeneous tissue, stimulation, and instant reaction quenching technology for biochemical kinetic analysis, large scale single-cell assay for the identification of biocatalysts and analytical techniques for single-cell studies in microbiology. The role of single-cell analysis in cancer, such as single-cell adhesion and cancer progression, single-cell technologies for cancer therapy, analytical technology for single cancer cell analysis, and biophysical markers for cancer cell analysis have been discussed. The flow cytometry based high throughput single-cell analysis have been well emphasized. Finally this book has covered single-cell electrophysiology, single-cell sensing and size measurement using mechanical and microwave resonators, molecular force spectroscopy for cell adhesion measurement, micro-tweezers and force microscopy techniques for single-cell mechanobiological analysis, mass spectrometry and acoustic tweezers for single-cell manipulation and analysis. This book is intended for academic and industrial researchers, undergraduate and graduate students in the fields of biomedical engineering, bio-micro/nanoengineering, and bio-micro/nano fabrication for single-cell analysis. It can be used for courses on bio-MEMS/bio-NEMS, biomicrofluidics, bio-micro/nanofabrications, micro/nanofluidics, biophysics, single cell analysis, bionanotechnology, drug delivery systems and biomedical microdevices. Collective contributions from respected experts, have brought diverse aspects of single-cell technologies in a single hand book. This will benefit researchers and practitioners in the biotechnology industry for different diseases analysis, therapeutics, diagnostics, drug discovery, drug screening etc. In addition to hard copies, the book will be available online and will often be updated by the authors.

The ability to classify and map numerous cell types as well as healthy and diseased cells can bring significant insight to biology and medicine. While single-cell sequencing becomes cornerstone for cell classification and mapping, isolation of interested cells for genomic analyses rely on fluorescence activated cell sorting (FACS), which can only isolate cells based on integrated intensities. The availability of flow cytometers with the capability to classify and isolate cells guided by high-content cell images is enabling and transformative. It provides a new paradigm to allow researchers and clinicians to isolate cells using multiple user-defined characteristics encoded by both fluorescent signals and morphological and spatial features. In this thesis, we demonstrated the “Image-Guided Cell Classification and Sorting” technology. This technology possesses high throughput isolation capability of FACS and high information content of microscopy. To achieve “Image-Guided Cell Classification and Sorting”, we combined the techniques of machine learning, photonics, real-time signal processing and microfluidics.

Principles of Cloning, Second Edition is the fully revised edition of the authoritative book on the science of cloning. The book presents the basic biological mechanisms of how cloning works and progresses to discuss current and potential applications in basic biology, agriculture, biotechnology, and medicine. Beginning with the history and theory behind cloning, the book goes on to examine methods of micromanipulation, nuclear transfer, genetic modification, and pregnancy and neonatal care of cloned animals. The cloning of various species—including mice, sheep, cattle, and non-mammals—is considered as well. The Editors have been involved in a number of breakthroughs using cloning technique, including the first demonstration that cloning works in differentiated cells done by the Recipient of the 2012 Nobel Prize for Physiology or Medicine – Dr John Gurdon; the cloning of the first mammal from a somatic cell – Drs Keith Campbell and Ian Wilmut; the demonstration that cloning can reset the biological clock - Drs Michael West and Robert Lanza; the demonstration that a terminally differentiated cell can give rise to a whole new individual – Dr Rudolf Jaenisch and the cloning of the first transgenic bovine from a differentiated cell – Dr Jose Cibelli. The majority of the contributing authors are the principal investigators on each of the animal species cloned to date and are expertly qualified to present the state-of-the-art information in their respective areas. - First and most comprehensive book on animal cloning, 100% revised - Describes an in-depth analysis of current limitations of the technology and research areas to explore - Offers cloning applications on basic biology, agriculture, biotechnology, and medicine

Complex organisms, such as humans and mice, consist of trillions of cells, yet we lack the tools to deeply characterize the immense space of cellular identity and behavior that defines health and disease. Indeed, evidence shows that cells, even those derived from identical clones, can present differences on the genomic, transcriptomic and epigenomic levels. It is therefore critical for researchers to be able to conduct studies at single-cell resolution in order to understand the vast diversity of life processes and further develop biological technologies. Here we describe the implementation of a plate-based 3’ single-cell RNA-sequencing (scRNASeq) barcoding strategy which allowed us to dramatically reduce costs, ease implementation, improve throughput, and generate more quantitative data. Further developments in 3’ barcoding strategies allowing parallel sequencing of thousands of cells at once, enabled us to study the mechanisms by which T cells cross into solid tumors, (i.e., their interactions with the endothelial cells lining blood vessels, and more specifically, venular endothelial cells; VEC). This revealed a unique transcriptional profile in VECs which highlights the importance of several transcription factors in establishing a gene signature conducive to T cell recruitment in immunogenic tumors. However, scRNA-Seq can only give us information about the state of a cell at the time of sequencing. Thus, to further explore the mechanisms at play behind single cell heterogeneity we worked on developing a strategy to record “historical” data in single cells. A CRISPR-based and a recombinase-based strategy were explored in this work. CRISPR presents the advantage of being very versatile and easy to multiplex, while recombinases are a well-established inducible DNA editing tool that is easier to implement. Using recombinases, we were able to demonstrate the recording of two independent signals in single cells. Overall, our work helped to establish 3’ prime barcoding as a valid strategy for scRNA-Seq laying the foundation for the development of high throughput technology in the lab that we then used to explore endothelial cell responses in cancer and to develop of new tools to couple historical “metadata” with scRNA-Seq.