Automatic enhancement preprocessing for segmentation of low quality cell images

We present a novel automatic preprocessing and ensemble learning technique for the segmentation of low-quality cell images. Capturing cells subjected to intense light is challenging due to their vulnerability to light-induced cell death. Consequently, microscopic cell images tend to be of low quality and it causes low accuracy for semantic segmentation. This problem can not be satisfactorily solved by classical image preprocessing methods. Therefore, we propose a novel approach of automatic enhancement preprocessing (AEP), which translates an input image into images that are easy to recognize by deep learning. AEP is composed of two deep neural networks, and the penultimate feature maps of the first network are employed as filters to translate an input image with low quality into images that are easily classified by deep learning. Additionally, we propose an automatic weighted ensemble learning (AWEL), which combines the multiple segmentation results. Since the second network predicts segmentation results corresponding to each translated input image, multiple segmentation results can be aggregated by automatically determining suitable weights. Experiments on two types of cell image segmentation confirmed that AEP can translate low-quality cell images into images that are easy to segment and that segmentation accuracy improves using AWEL.

Development of an in vivo cleavable donor plasmid for targeted transgene integration by CRISPR-Cas9 and CRISPR-Cas12a

The CRISPR-Cas system is widely used for genome editing of cultured cells and organisms. The discovery of a new single RNA-guided endonuclease, CRISPR-Cas12a, in addition to the conventional CRISPR-Cas9 has broadened the number of editable target sites on the genome. Here, we developed an in vivo cleavable donor plasmid for precise targeted knock-in of external DNA by both Cas9 and Cas12a. This plasmid, named pCriMGET_9-12a (plasmid of synthetic CRISPR-coded RNA target sequence-equipped donor plasmid-mediated gene targeting via Cas9 and Cas12a), comprises the protospacer-adjacent motif sequences of Cas9 and Cas12a at the side of an off-target free synthetic CRISPR-coded RNA target sequence and a multiple cloning site for donor cassette insertion. pCriMGET_9-12a generates a linearized donor cassette in vivo by both CRISPR-Cas9 and CRISPR-Cas12a, which resulted in increased knock-in efficiency in culture cells. This method also achieved > 25% targeted knock-in of long external DNA (> 4 kb) in mice by both CRISPR-Cas9 and CRISPR-Cas12a. The pCriMGET_9-12a system expands the genomic target space for transgene knock-in and provides a versatile, low-cost, and high-performance CRISPR genome editing tool.

Experimental pathology by intravital microscopy and genetically encoded fluorescent biosensors

The invention of two‐photon excitation microscopes widens the potential application of intravital microscopy (IVM) to the broad field of experimental pathology. Moreover, the recent development of fluorescent protein‐based, genetically encoded biosensors provides an ideal tool to visualize the cell function in live animals. We start from a brief review of IVM with two‐photon excitation microscopes and genetically encoded biosensors based on the principle of Förster resonance energy transfer (FRET). Then, we describe how IVM using biosensors has revealed the pathogenesis of several disease models.

DeTerm: Software for automatic detection of neuronal dendritic branch terminals via an artificial neural network

Dendrites of neurons receive and process synaptic or sensory inputs. The Drosophila class IV dendritic arborization (da) neuron is an established model system to explore molecular mechanisms of dendrite morphogenesis. The total number of dendritic branch terminals is one of the frequently employed parameters to characterize dendritic arborization complexity of class IV neurons. This parameter gives a useful phenotypic readout of arborization during neurogenesis, and it is typically determined by laborious manual analyses of numerous images. Ideally, an automated analysis would greatly reduce the workload; however, it is challenging to automatically discriminate dendritic branch terminals from signals of surrounding tissues in whole-mount live larvae. Here, we describe our newly developed software, called DeTerm, which automatically recognizes and quantifies dendrite branch terminals via an artificial neural network. Once we input an image file of a neuronal dendritic arbor and its region of interest information, DeTerm is capable of labeling terminals of larval class IV neurons with high precision, and it also provides positional data of individual terminals. We further show that DeTerm is applicable to other types of neurons, including mouse cerebellar Purkinje cells. DeTerm is freely available on the web and was successfully tested on Mac, Windows and Linux.