We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed sensing.The method can apply variable measurement resolution over the entire spectral range to reduce the measurement ti...We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed sensing.The method can apply variable measurement resolution over the entire spectral range to reduce the measurement time by over 75%compared to a global high-resolution measurement.Mimicking the characteristics of the human retina system,the resolution distribution follows the principle of gradually decreasing.The system allows the spectral peaks of interest to be captured dynamically or to be specified a priori by a user.The system was tested by measuring single and dual spectral peaks,and the results of spectral peaks are consistent with those of global high-resolution measurements.展开更多
Developing an imaging method with micrometer-to-subcellular resolution is of great significance for visualizing biological samples of different sizes.The label-free and highthroughput mass spectrometry imaging(MSI)tec...Developing an imaging method with micrometer-to-subcellular resolution is of great significance for visualizing biological samples of different sizes.The label-free and highthroughput mass spectrometry imaging(MSI)technology has shown potential in the implementation of this view.Despite many improvements in MSI witnessed over the past decades,it remains a challenge to achieve a flexible resolution from micrometer down to subcellular level with high detection sensitivity.In this Perspective,we focus on the recent development of MSI techniques based on different ionization resources.Furthermore,several designs of instruments and applications in bioimaging have been reviewed and compared.Additionally,we proposed the perspectives and challenges for MSI methods,including pursuing the matrix free and multiscale resolution with high detection sensitivity and deeply combining machine learning in omics research.展开更多
基金Project supported by the Natural Science Foundation of Shandong Province,China(Grant Nos.ZR2020MF119 and ZR2020MA082)the National Natural Science Foundation of China(Grant No.62002208)the National Key Research and Development Program of China(Grant No.2018YFB0504302).
文摘We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed sensing.The method can apply variable measurement resolution over the entire spectral range to reduce the measurement time by over 75%compared to a global high-resolution measurement.Mimicking the characteristics of the human retina system,the resolution distribution follows the principle of gradually decreasing.The system allows the spectral peaks of interest to be captured dynamically or to be specified a priori by a user.The system was tested by measuring single and dual spectral peaks,and the results of spectral peaks are consistent with those of global high-resolution measurements.
基金the financial support from the National Natural Science Foundation of China(22034003,21974060,21327902)the Excellent Research Program of Nanjing University(ZYJH004)the China Postdoctoral Science Foundation(2019TQ0145).
文摘Developing an imaging method with micrometer-to-subcellular resolution is of great significance for visualizing biological samples of different sizes.The label-free and highthroughput mass spectrometry imaging(MSI)technology has shown potential in the implementation of this view.Despite many improvements in MSI witnessed over the past decades,it remains a challenge to achieve a flexible resolution from micrometer down to subcellular level with high detection sensitivity.In this Perspective,we focus on the recent development of MSI techniques based on different ionization resources.Furthermore,several designs of instruments and applications in bioimaging have been reviewed and compared.Additionally,we proposed the perspectives and challenges for MSI methods,including pursuing the matrix free and multiscale resolution with high detection sensitivity and deeply combining machine learning in omics research.
