Here we analyzed the innate fluorescence signature of single microbial cell, within both clonal and mixed populations of microorganisms. We found that even very similarly shaped cells differ noticeably in their autofluorescence features, and that the innate fluorescence signatures change dynamically with growth phases. We demonstrated that machine learning models can be trained with a dataset of single-cell innate fluorescence signatures to annotate cells according to their phenotypes and physiological status, for example distinguishing a wild type cells from its nitrogen metabolism mutant counterpart, and log phase cells from stationary phase cells in We developed a minimally invasive method (onfocal eflection microscopy-assisted single-cell nnate luorescence analysis: CRIF) to optically extract and catalog the innate cellular fluorescence signatures from each of the individual live microbial cells in a three-dimensional space. This technique represents a step-forward from traditional techniques which analyze the innate fluorescence signatures at the population-level and necessitates a clonal culture. Since the fluorescence signature is an innate property of a cell, our technique allows the prediction of the types or physiological status of intact and tag-free single cells, within a cell population distributed in a three-dimensional space. Our study presents a blueprint for a streamlined cell analysis, where one can directly assess the potential phenotype of each single cell in a heterogenous population by their autofluorescence signature under a microscope without cell tagging.A cell's innate fluorescence signature is an assemblage of fluorescence signals emitted by diverse bio-molecules within a cell. It is known that the innate fluoresce signature reflects various cellular properties and physiological statuses, thus they can serve as a rich source of information in cell characterization as well as cell identification. However, conventional techniques focus on the analysis of the innate fluorescence signatures at the population-level, but not at the single-cell level, and thus necessitates a clonal culture. In the present study, we developed a technique to analyze the innate fluorescence signature of a single microbial cell. Using the novel method, we found that even very similarly shaped cells differ noticeably in their autofluorescence features, and the innate fluorescence signature changes dynamically with growth phases. We also demonstrated that the different cell types can be classified accurately within a mixed population under a microscope at the resolution of single-cell, depending solely on the innate fluorescence signature information. We suggest that the single-cell autofluoresce signature analysis is a promising tool to directly assess the taxonomical or physiological heterogeneity within a microbial population, without cell tagging.