Super-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a Deep learning based SUper-REsolution model called DeepSURE, where the hematoxylin and eosin (H&E) stain microscopy image is used to pose constrains in the process of super-resolution reconstruction to alleviate the ill-poseness. A novel model architecture is designed to achieve multi-task optimization by incorporating multi-modal image registration and fusion in a mutual reinforced framework. The present results demonstrated that the DeepSURE method is able to produce super-resolution reconstruction image with rich chemical information and detailed structure both on visual inspection and quantitative evaluation. In addition, the method was found able to improve the delimitation of boundary between cancerous and para-cancerous regions in MSI image. Furthermore, the reconstruction of low-resolution spatial transcriptomics data demonstrates that the developed DeepSURE method may find wider applications in biomedical fields.

Stable isotope chemical labeling methods have been widely used for high-throughput mass spectrometry (MS)based quantitative proteomics in biological and clinical applications. However, the existing methods are far from meeting the requirements for high sensitivity detection. In the present study, a novel isobaric stable isotope N-phosphorylation labeling (iSIPL) strategy was developed for quantitative proteome analysis. The tryptic peptides were selectively labeled with iSIPL tag to generate the novel reporter ions containing phosphoramidate PÀ N bond with high intensities under lower collision energies. iSIPL strategy are suitable for peptide sequencing and quantitative analysis with high sensitivity and accuracy even for samples of limited quantity. Furthermore, iSIPL coupled with affinity purification and mass spectrometry was applied to measure the dynamics of cyclin dependent kinase 9 (CDK9) interactomes during transactivation of the HIV-1 provirus. The interaction of CDK9 with PARP13 was found to significantly decrease during Tat-induced activation of HIV-1 gene transcription, suggesting the effectiveness of iSIPL strategy in dynamic analysis of protein-protein interaction in vivo. More than that, the proposed iSIPL strategy would facilitate large-scale accurate quantitative proteomics by increasing multiplexing capability.

Stable isotope chemical labeling methods have been widely used for high-throughput mass spectrometry (MS)based quantitative proteomics in biological and clinical applications. However, the existing methods are far from meeting the requirements for high sensitivity detection. In the present study, a novel isobaric stable isotope N-phosphorylation labeling (iSIPL) strategy was developed for quantitative proteome analysis. The tryptic peptides were selectively labeled with iSIPL tag to generate the novel reporter ions containing phosphoramidate PÀ N bond with high intensities under lower collision energies. iSIPL strategy are suitable for peptide sequencing and quantitative analysis with high sensitivity and accuracy even for samples of limited quantity. Furthermore, iSIPL coupled with affinity purification and mass spectrometry was applied to measure the dynamics of cyclin dependent kinase 9 (CDK9) interactomes during transactivation of the HIV-1 provirus. The interaction of CDK9 with PARP13 was found to significantly decrease during Tat-induced activation of HIV-1 gene transcription, suggesting the effectiveness of iSIPL strategy in dynamic analysis of protein-protein interaction in vivo. More than that, the proposed iSIPL strategy would facilitate large-scale accurate quantitative proteomics by increasing multiplexing capability.

Super-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a Deep learning based SUper-REsolution model called DeepSURE, where the hematoxylin and eosin (H&E) stain microscopy image is used to pose constrains in the process of super-resolution reconstruction to alleviate the ill-poseness. A novel model architecture is designed to achieve multi-task optimization by incorporating multi-modal image registration and fusion in a mutual reinforced framework. The present results demonstrated that the DeepSURE method is able to produce super-resolution reconstruction image with rich chemical information and detailed structure both on visual inspection and quantitative evaluation. In addition, the method was found able to improve the delimitation of boundary between cancerous and para-cancerous regions in MSI image. Furthermore, the reconstruction of low-resolution spatial transcriptomics data demonstrates that the developed DeepSURE method may find wider applications in biomedical fields.

There is growing awareness that metabolic heterogeneity of organism provides vital insight into the disease with molecular mechanism and personalized therapy. The screening of metabolism-related sub-regions that affect disease development is essential for the more focused exploration how disease progress aberrant phenotypes, even carcinogenesis and metastasis. Mass spectrometry imaging (MSI) technique has distinct advantages to reveal the heterogeneity of organism based on the in situ molecular profiles. The challenge of heterogeneous analysis has been to perform an objective identification among biological tissues with different characteristics. By introducing the divide-and-conquer strategy to architecture design and application, we establish here a flexible unsupervised deep learning model, called divide-and-conquer (dc)-DeepMSI, for metabolic heterogeneity analysis from MSI data without prior knowledge of histology. dc-DeepMSI can be used to identify either spatially contiguous region-of-interest (ROIs) or spatially sporadic ROIs. We demonstrate that the novel learning strategy successfully obtain sub-regions that are statistically linked to invasion status and molecular phenotypes of breast cancer, as well as organizing principles during developmental phase.