High-throughput single-cell RNA-sequencing (scRNA-seq) methodologies enable characterization of complex biological samples by increasing the number of cells that can be profiled contemporaneously. Nevertheless, these approaches recover less information per cell than low-throughput strategies. To accurately report the expression of key phenotypic features of cells, scRNA-seq platforms are needed that are both high fidelity and high throughput. To address this need, we created Seq-Well S3 (“Second-Strand Synthesis”), a massively parallel scRNA-seq protocol that uses a randomly primed second-strand synthesis to recover complementary DNA (cDNA) molecules that were successfully reverse transcribed but to which a second oligonucleotide handle, necessary for subsequent whole transcriptome amplification, was not appended due to inefficient template switching. Seq-Well S3 increased the efficiency of transcript capture and gene detection compared with that of previous iterations by up to 10- and 5-fold, respectively. We used Seq-Well S3 to chart the transcriptional landscape of five human inflammatory skin diseases, thus providing a resource for the further study of human skin inflammation. Display omitted •Seq-Well S3 uses second-strand synthesis to improve transcript capture•Seq-Well S3 was benchmarked against a best-in-class commercial platform•Seq-Well S3 was applied to profile inflammatory cell states in skin diseases•Analysis of skin inflammation uncovered unique and conserved cellular phenotypes Hughes et al. report the development of a technique for high-throughput single-cell RNA-sequencing, “Seq-Well S3,” that enables increased sensitivity and improved detection of genes including transcription factors, cytokines, and cytokine receptors. Using Seq-Well S3, the authors define inflammatory cell states across multiple skin diseases.