A novel method, STRT (Single-Cell Tagged Reverse Transcription), was developed to characterize the single-cell transcriptional landscape using highly multiplex RNA-seq. This method enables the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology, and disease. STRT involves generating barcoded cDNA libraries from 96 single cells, which are then sequenced to produce a two-dimensional cell map. This map integrates three levels of organization: the whole population of cells, the functionally distinct subpopulations, and the single cells themselves, without the need for known markers to classify cell types. The method was demonstrated by analyzing the transcriptomes of 85 single cells of two distinct types. The feasibility of the strategy was confirmed by the successful analysis of 85 single cells, with the resulting data showing high specificity for expressed mRNA and rejection of genomic DNA and unspliced pre-mRNA. The method also allows for the analysis of both known and novel factors specifically expressed in a cell type. The study also revealed that single-cell cDNA synthesis can lead to variations in mRNA abundance, with highly expressed genes being detected in every cell, while lower expressed genes had a lower probability of detection. The method was able to accurately quantify gene expression levels, with results agreeing with those obtained by Q-PCR and microarray hybridization. The results showed that ES cells expressed fewer genes and had a smaller number of mRNA molecules compared to MEFs. The study also demonstrated that the method could distinguish cell types based on expression data, without relying on pre-existing markers. The method was able to reveal cell type relationships in a two-dimensional cell map, showing that cells of the same type clustered together. The study concluded that STRT is an efficient strategy to access single-cell expression data in heterogeneous populations of cells. The method has the potential to be applied to a wide range of mixed samples, including specific progenitors active during organogenesis, small populations of stem cells embedded in adult tissues, and heterogeneous tumor cell samples. The method is scalable and can be used to generate high-throughput data, making it a valuable tool for studying the transcriptional landscape of single cells.A novel method, STRT (Single-Cell Tagged Reverse Transcription), was developed to characterize the single-cell transcriptional landscape using highly multiplex RNA-seq. This method enables the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology, and disease. STRT involves generating barcoded cDNA libraries from 96 single cells, which are then sequenced to produce a two-dimensional cell map. This map integrates three levels of organization: the whole population of cells, the functionally distinct subpopulations, and the single cells themselves, without the need for known markers to classify cell types. The method was demonstrated by analyzing the transcriptomes of 85 single cells of two distinct types. The feasibility of the strategy was confirmed by the successful analysis of 85 single cells, with the resulting data showing high specificity for expressed mRNA and rejection of genomic DNA and unspliced pre-mRNA. The method also allows for the analysis of both known and novel factors specifically expressed in a cell type. The study also revealed that single-cell cDNA synthesis can lead to variations in mRNA abundance, with highly expressed genes being detected in every cell, while lower expressed genes had a lower probability of detection. The method was able to accurately quantify gene expression levels, with results agreeing with those obtained by Q-PCR and microarray hybridization. The results showed that ES cells expressed fewer genes and had a smaller number of mRNA molecules compared to MEFs. The study also demonstrated that the method could distinguish cell types based on expression data, without relying on pre-existing markers. The method was able to reveal cell type relationships in a two-dimensional cell map, showing that cells of the same type clustered together. The study concluded that STRT is an efficient strategy to access single-cell expression data in heterogeneous populations of cells. The method has the potential to be applied to a wide range of mixed samples, including specific progenitors active during organogenesis, small populations of stem cells embedded in adult tissues, and heterogeneous tumor cell samples. The method is scalable and can be used to generate high-throughput data, making it a valuable tool for studying the transcriptional landscape of single cells.