Fluorescence labeling and imaging provide an opportunity to observe the structure of biological tissues, playing a crucial role in the field of histopathology. However, when labeling and imaging biological tissues, there are still some challenges, e.g., time-consuming tissue preparation steps, expensive reagents, and signal bias due to photobleaching. To overcome these limitations, we present a deep-learning-based method for fluorescence translation of tissue sections, which is achieved by conditional generative adversarial network (cGAN). Experimental results from mouse kidney tissues demonstrate that the proposed method can predict the other types of fluorescence images from one raw fluorescence image, and implement the virtual multi-label fluorescent staining by merging the generated different fluorescence images as well. Moreover, this proposed method can also effectively reduce the time-consuming and laborious preparation in imaging processes, and further saves the cost and time.

The online version contains supplementary material available at 10.1007/s43657-023-00094-1.

While multi-channel fluorescence microscopy is a vital imaging method in biological studies, the number of channels that can be imaged simultaneously is limited by technical and hardware limitations such as emission spectra cross-talk. One solution is using deep neural networks to model the localization relationship between two proteins so that the localization of one protein can be digitally predicted. Furthermore, the input and predicted localization implicitly reflect the modeled relationship. Accordingly, observing the response of the prediction via manipulating input localization could provide an informative way to analyze the modeled relationships between the input and the predicted proteins.

We propose a protein localization prediction (PLP) method using a cGAN named 4D Reslicing Generative Adversarial Network (4DR-GAN) to digitally generate additional channels. 4DR-GAN models the joint probability distribution of input and output proteins by simultaneously incorporating the protein localization signals in four dimensions including space and time. Because protein localization often correlates with protein activation state, based on accurate PLP, we further propose two novel tools: digital activation (DA) and digital inactivation (DI) to digitally activate and inactivate a protein, in order to observing the response of the predicted protein localization. Compared with genetic approaches, these tools allow precise spatial and temporal control. A comprehensive experiment on six pairs of proteins shows that 4DR-GAN achieves higher-quality PLP than Pix2Pix, and the DA and DI responses are consistent with the known protein functions. The proposed PLP method helps simultaneously visualize additional proteins, and the developed DA and DI tools provide guidance to study localization-based protein functions.

The open-source code is available at https://github.com/YangJiaoUSA/4DR-GAN.

Supplementary data are available at Bioinformatics online.

Salmonella enteritidis is a foodborne pathogen that causes high morbidity in poultry. Proteomic analysis by liquid chromatography tandem mass spectrometry (LC-MS/MS) was used to study the effects of Salmonella infection on spleen proteome in broiler chickens. Day-old broilers were assigned to control (CON; n = 60) or Salmonella challenge (CON−SE; n = 60), and gavaged with Tryptic soy agar broth or SE. A subset of chicks was euthanized on D3 and D7 (n = 4/group/day) and the spleen was removed, and rapidly frozen, subsequently proteome was measured using label-free LC-MS/MS. Protein spectra were mapped to Gallus gallus Uniprot database. Differentially abundant proteins (DAP; FDR < 0.05) between days and treatments were identified using ANOVA. Cecal content of Salmonella in CON−SE was 3.37 log10 CFU/g and CON were negative. Across the 16 samples, 2625 proteins were identified. Proteins that decreased in abundance between days mediated cell cycle progression, while those that increased in abundance function in cytoskeleton and mRNA processing. SE infection caused an increase in proteins that mediated redox homeostasis, lysosomal activities, and energy production, while proteins decreased in abundance-mediated developmental progression. Proteomic signatures of spleen suggest SE infection was metabolically costly, and energy was diverted from normal developmental processes to potentiate disease resistance mechanisms.

Colorectal cancer (CRC) is a devastating disease, mainly because of metastasis. As a result, there is a need to better understand the molecular basis of invasion and metastasis and to identify new biomarkers and therapeutic targets to aid in managing these tumors. The actin cytoskeleton and actin-binding proteins are known to play an important role in the process of cancer metastasis because they control and execute essential steps in cell motility and contractility as well as cell division. Caldesmon (CaD) is an actin-binding protein encoded by the CALD1 gene as multiple transcripts that mainly encode two protein isoforms: High-molecular-weight CaD, expressed in smooth muscle, and low-molecular weight CaD (l-CaD), expressed in nonsmooth muscle cells. According to our comprehensive review of the literature, CaD, particularly l-CaD, plays a key role in the development, metastasis, and resistance to chemoradiotherapy in colorectal, breast, and urinary bladder cancers and gliomas, among other malignancies. CaD is involved in many aspects of the carcinogenic hallmarks, including epithelial mesenchymal transition via transforming growth factor-beta signaling, angiogenesis, resistance to hormonal therapy, and immune evasion. Recent data show that CaD is expressed in tumor cells as well as in stromal cells, such as cancer-associated fibroblasts, where it modulates the tumor microenvironment to favor the tumor. Interestingly, CaD undergoes selective tumor-specific splicing, and the resulting isoforms are generally not expressed in normal tissues, making these transcripts ideal targets for drug design. In this review, we will analyze these features of CaD with a focus on CRC and show how the currently available data qualify CaD as a potential candidate for targeted therapy in addition to its role in the diagnosis and prognosis of cancer.

Molecular visualization is a cornerstone of structural biology, providing insights into the form and function of biomolecules that are difficult to achieve any other way. Scientific analysis, publication, education, and outreach often benefit from photorealistic molecular depictions rendered using advanced computer-graphics programs such as Maya, 3ds Max, and Blender. However, setting up molecular scenes in these programs is laborious even for expert users, and rendering often requires substantial time and computer resources. We have created a deep-learning model called Prot2Prot that quickly imitates photorealistic visualization styles, given a much simpler, easy-to-generate molecular representation. The resulting images are often indistinguishable from images rendered using industry-standard 3D graphics programs, but they can be created in a fraction of the time, even when running in a web browser. To the best of our knowledge, Prot2Prot is the first example of image-to-image translation applied to macromolecular visualization. Prot2Prot is available free of charge, released under the terms of the Apache License, Version 2.0. Users can access a Prot2Prot-powered web app without registration at http://durrantlab.com/prot2prot .