Methods in molecular biology
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Duchenne muscular dystrophy (DMD) is a devastating X-linked muscle disorder affecting many children. The disease is caused by the lack of dystrophin production and characterized by muscle wasting. The most common causes of death are respiratory failure and heart failure. ⋯ Here, we present methodologies to systemically inject PMOs into humanized DMD model mice and determine levels of dystrophin restoration via Western blotting. Using a tris-acetate gradient SDS gel and semi-dry transfer with three buffers, including the Concentrated Anode Buffer, Anode Buffer, and Cathode Buffer, less than 1% normal levels of dystrophin expression are easily detectable. This method is fast, easy, and sensitive enough for the detection of dystrophin from both cultured muscle cells and muscle biopsy samples.
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The clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) protein has emerged as a genome engineering tool for various organisms. Known as the CRISPR-Cas system, Cas endonucleases such as Cas9 and Cas12a (also known as Cpf1) and guide RNA (gRNA) complexes recognize and cleave the target DNA, allowing for targeted gene manipulation. ⋯ Recently, we have developed fusion guide RNAs (fgRNAs) for orthogonal gene manipulation using Cas9 and Cas12a. Here, we describe the methods for designing and generating fgRNAs-expression constructs to achieve multiplex genome editing and gene manipulation in human cells.
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With rapid advances in experimental instruments and protocols, imaging and sequencing data are being generated at an unprecedented rate contributing significantly to the current and coming big biomedical data. Meanwhile, unprecedented advances in computational infrastructure and analysis algorithms are realizing image-based digital diagnosis not only in radiology and cardiology but also oncology and other diseases. Machine learning methods, especially deep learning techniques, are already and broadly implemented in diverse technological and industrial sectors, but their applications in healthcare are just starting. ⋯ Moreover, the applications of genomics data are realizing the potential for personalized medicine, making diagnosis, treatment, monitoring, and prognosis more accurate. In this chapter, we discuss machine learning methods readily available for digital pathology applications, new prospects of integrating spatial genomics data on tissues with tissue morphology, and frontier approaches to combining genomics data with pathological imaging data. We present perspectives on how artificial intelligence can be synergized with molecular genomics and imaging to make breakthroughs in biomedical and translational research for computer-aided applications.
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The discovery of induced pluripotent stem cells (iPSCs) allows for establishment of human embryonic stem-like cells from various adult human somatic cells (e.g., fibroblasts), without the need for destruction of human embryos. This provides an unprecedented opportunity where patient-specific iPSCs can be subsequently differentiated to many cell types, e.g., cardiac cells and neurons, so that we can use these iPSC-derived cells to study patient-specific disease mechanisms and conduct drug testing and screening. Critically, these cells have unlimited therapeutic potentials, and there are many ongoing clinical trials to investigate the regenerative potentials of these iPSC-derivatives in humans. ⋯ The non-integrating mRNA reprogramming is of high efficiency, but it is sensitive to reagents and need approaches to reduce the immunogenic reaction. An alternative non-integrating and safer way of generating iPSCs is via direct delivery of recombinant cell-penetrating reprogramming proteins into the cells to be reprogrammed, but reprogramming efficiency of the protein-based approach is extremely low compared to the conventional virus-based nuclear reprogramming. Herein, we describe detailed steps for efficient generation of human iPSCs by protein-based reprogramming in combination with stimulation of the Toll-like receptor 3 (TLR3) innate immune pathway.
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DNA methylation is a transgenerational stable epigenetic modification able to regulate gene expression and genome stability. The analysis of DNA methylation by genome-wide bisulfite sequencing become the main genomic approach to study epigenetics in many organisms; leading to standardization of the alignment and methylation call procedures. ⋯ Therefore, in this chapter we propose a computational workflow for the analysis, visualization, and interpretation of data obtained from alignment of whole genome bisulfite sequencing of plant samples. Using almost exclusively the R working environment we will examine in depth how to tackle some plant-related issues during epigenetic analysis.