Deep learning permits imaging of multiple structures with the same fluorophores.

Fluorescence microscopy, which employs fluorescent tags to label and observe cellular structures and their dynamics, is a powerful tool for life sciences. However, due to the spectral overlap between different dyes, a limited number of structures can be separately labeled and imaged for live-cell applications. In addition, the conventional sequential channel imaging procedure is quite time consuming, as it needs to switch either different lasers or filters. Here, we propose a novel double-structure network (DBSN) that consists of multiple connected models, which can extract six distinct subcellular structures from three raw images with only two separate fluorescent labels. DBSN combines the intensity-balance model to compensate for uneven fluorescent labels for different structures and the structure-separation model to extract multiple different structures with the same fluorescent labels. Therefore, DBSN breaks the bottleneck of the existing technologies and holds immense potential applications in the field of cell biology.

A Saturation Artifacts Inpainting Method Based on Two-Stage GAN for Fluorescence Microscope Images.

Fluorescence microscopic images of cells contain a large number of morphological features that are used as an unbiased source of quantitative information about cell status, through which researchers can extract quantitative information about cells and study the biological phenomena of cells through statistical and analytical analysis. As an important research object of phenotypic analysis, images have a great influence on the research results. Saturation artifacts present in the image result in a loss of grayscale information that does not reveal the true value of fluorescence intensity. From the perspective of data post-processing, we propose a two-stage cell image recovery model based on a generative adversarial network to solve the problem of phenotypic feature loss caused by saturation artifacts. The model is capable of restoring large areas of missing phenotypic features. In the experiment, we adopt the strategy of progressive restoration to improve the robustness of the training effect and add the contextual attention structure to enhance the stability of the restoration effect. We hope to use deep learning methods to mitigate the effects of saturation artifacts to reveal how chemical, genetic, and environmental factors affect cell state, providing an effective tool for studying the field of biological variability and improving image quality in analysis.

Boosting microscopic object detection via feature activation map guided poisson blending.

Microscopic examination of visible components based on micrographs is the gold standard for testing in biomedical research and clinical diagnosis. The application of object detection technology in bioimages not only improves the efficiency of the analyst but also provides decision support to ensure the objectivity and consistency of diagnosis. However, the lack of large annotated datasets is a significant impediment in rapidly deploying object detection models for microscopic formed elements detection. Standard augmentation methods used in object detection are not appropriate because they are prone to destroy the original micro-morphological information to produce counterintuitive micrographs, which is not conducive to build the trust of analysts in the intelligent system. Here, we propose a feature activation map-guided boosting mechanism dedicated to microscopic object detection to improve data efficiency. Our results show that the boosting mechanism provides solid gains in the object detection model deployed for microscopic formed elements detection. After image augmentation, the mean Average Precision (mAP) of baseline and strong baseline of the Chinese herbal medicine micrograph dataset are increased by 16.3% and 5.8% respectively. Similarly, on the urine sediment dataset, the boosting mechanism resulted in an improvement of 8.0% and 2.6% in mAP of the baseline and strong baseline maps respectively. Moreover, the method shows strong generalizability and can be easily integrated into any main-stream object detection model. The performance enhancement is interpretable, making it more suitable for microscopic biomedical applications.

A deep learning based method for automatic analysis of high-throughput droplet digital PCR images.

Droplet digital PCR (ddPCR) is a technique for absolute quantification of nucleic acid molecules and is widely used in biomedical research and clinical diagnosis. ddPCR partitions the reaction solution containing target molecules into a large number of independent microdroplets for amplification and performs quantitative analysis of target molecules by calculating the proportion of positive droplets by the principle of Poisson distribution. Accurate recognition of positive droplets in ddPCR images is of great importance to guarantee the accuracy of target nucleic acid quantitative analysis. However, hand-designed operators are sensitive to interference and have disadvantages such as low contrast, uneven illumination, low sample copy number, and noise, and their accuracy and robustness still need to be improved. Herein, we developed a deep learning-based high-throughput ddPCR droplet detection framework for robust and accurate ddPCR image analysis, and the experimental results show that our method achieves excellent performance in the recognition of positive droplets (99.71%) within a limited time. By combining the Hough transform and a convolutional neural network (CNN), our novel method can automatically filter out invalid droplets that are difficult to be identified by local or global encoding methods and realize high-precision localization and classification of droplets in ddPCR images under variable exposure, contrast, and uneven illumination conditions without the need for image pre-processing and normalization processes.

Deep learning-enabled fast DNA-PAINT imaging in cells.

DNA-based point accumulation in nanoscale topography (DNA-PAINT) is a well-established technique for single-molecule localization microscopy (SMLM), enabling resolution of up to a few nanometers. Traditionally, DNA-PAINT involves the utilization of tens of thousands of single-molecule fluorescent images to generate a single super-resolution image. This process can be time-consuming, which makes it unfeasible for many researchers. Here, we propose a simplified DNA-PAINT labeling method and a deep learning-enabled fast DNA-PAINT imaging strategy for subcellular structures, such as microtubules. By employing our method, super-resolution reconstruction can be achieved with only one-tenth of the raw data previously needed, along with the option of acquiring the widefield image. As a result, DNA-PAINT imaging is significantly accelerated, making it more accessible to a wider range of biological researchers.

Transcriptional regulation of the mannan utilization genes in the alkaliphilic Bacillus sp. N16-5.

Bacillus sp. N16-5 is an alkaliphile with a great ability to utilize mannan. Its mannan utilization gene cluster has been identified in a previous study. The ManR protein encoded by the cluster was predicted to be a LacI family regulator, and the transcription level of the mannan utilization gene cluster was upregulated after the manR gene was deleted, indicating that ManR is the repressor of this cluster. The transcription of the related genes was downregulated when manH, encoding the extracellular substrate-binding domain of the manno-oligosaccharide transporter, was deleted. Furthermore, isothermal titration calorimetry revealed that mannotetraose and mannopentose are ligands of ManR. These results all corroborate the hypothesis that the mannan utilization gene cluster is repressed by the transcription regulator ManR, and that the repression is removed when it binds to manno-oligosaccharides, which are generated by mannan degradation and transported into the cell by a specific transporter.

Gene expression pattern analysis of a recombinant Escherichia coli strain possessing high growth and lycopene production capability when using fructose as carbon source.

OBJECTIVE: Escherichia coli K12f-pACLYC has a high capability for growth and lycopene production when using fructose as carbon source and the transcription of genes involved was compared in glucose-grown and fructose-grown cells. RESULTS: Escherichia coli K12f-pACLYC was grown on 10 g fructose l(-1) and reached 4.6 g DCW l(-1) with lycopene at 192 mg g DCW(-1), values that are 3-fold and 7-fold higher than when growing on glucose. Gene transcription profiles of fructose-grown and glucose-grown cells were compared. 384 differentially expressed genes (DEGs) with fold changes ≥4 were identified, and the transcription of genes involved in fructose uptake and metabolism, pyruvate catabolism, tricarboxylic acid cycle and oxidative phosphorylation varied significantly. These changes enhanced the metabolic flux into the Embden-Meyerhof-Parnas pathway and the tricarboxylic acid cylcle and coupled to oxidative phosphorylation. These enhanced activities provide more precursors, cofactors and energy needed for growth lycopene production. CONCLUSION: The high capability of E. coli K12f-pACLYC for growth and lycopene production when growing on fructose is due to transcriptional regulation, and the relevant genes were identified.