CD4 + effector lymphocytes (Teff) are traditionally classified by the cytokines they produce. To determine the states that Teff actually adopt in frontline tissues in vivo , we applied single-cell transcriptome and chromatin analysis on colonic Teff cells, in germ-free or conventional mice, or after challenge with a range of phenotypically biasing microbes. Subsets were marked by expression of interferon-signature or myeloid-specific transcripts, but transcriptome or chromatin structure could not resolve discrete clusters fitting classic T H subsets. At baseline or at different times of infection, transcripts encoding cytokines or proteins commonly used as T H markers distributed in a polarized continuum, which was also functionally validated. Clones derived from single progenitors gave rise to both IFN-γ and IL17-producing cells. Most transcriptional variance was tied to the infecting agent, independent of the cytokines produced, and chromatin variance primarily reflected activity of AP1 and IRF transcription factor families, not the canonical subset master regulators T-bet, GATA3, RORγ.

Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular atlas of chromatin accessibility across 81 immune cell types, we asked if a convolutional neural network (CNN) could learn to infer cell type-specific chromatin accessibility solely from regulatory DNA sequences. With a tailored architecture and an ensemble approach to CNN parameter interpretation, we show that our trained network (“AI-TAC”) does so by rediscovering ab initio the binding motifs for known regulators and some unknown ones. Motifs whose importance is learned virtually as functionally important overlap strikingly well with positions determined by chromatin immunoprecipitation for several TFs. AI-TAC establishes a hierarchy of TFs and their interactions that drives lineage specification and also identifies stage-specific interactions, like Pax5/Ebf1 vs. Pax5/Prdm1, or the role of different NF-κB dimers in different cell types. AI-TAC assigns Spi1/Cebp and Pax5/Ebf1 as the drivers necessary for myeloid and B lineage fates, respectively, but no factors seemed as dominantly required for T cell differentiation, which may represent a fall-back pathway. Mouse-trained AI-TAC can parse human DNA, revealing a strikingly similar ranking of influential TFs and providing additional support that AI-TAC is a generalizable regulatory sequence decoder. Thus, deep learning can reveal the regulatory syntax predictive of the full differentiative complexity of the immune system.

Metalloporphyrinic frameworks have demonstrated to be an alternative candidate for natural enzymes due to their diverse structures and unique peroxidase-mimicking properties. In this study, a manganese–metalloporphyrin framework (PCN-222­(Mn)) was synthesized as a biomimetic metal–organic framework (MOF). This catalyst exhibited highly intrinsic peroxidase-like activity with 3,3′,5,5′-tetramethylbenzidine as the chromogenic substrate. Additionally, a higher peroxidase-like activity was observed in a wider pH range (from 3.0 to 8.0), which is undoubtedly advantageous for PCN-222­(Mn) to detect H2O2 under physiological and pathological environments. Based on the excellent peroxidase-mimicking activities of PCN-222­(Mn), a novel hydrogen peroxide (H2O2) nonenzymic amperometric biosensor was constructed through electro-polymerizing a conductive and stable poly-glutamic acid (PGA) film on the surface of a PCN-222­(Mn)-modified electrode (PGA/PCN-222­(Mn)/GCE). Resulting from the synergistic activity of PCN-222­(Mn) and PGA film, a sensitive, selective, and reliable method was established for H2O2 detection with a linear range of 5 × 10–7 to 1.01 × 10–3 mol/L and detection limit of 3.1 × 10–8 mol/L. In addition, PGA/PCN-222­(Mn) has a long-term stability and can be used over 90 cycles without any decrease in analytical performance. These outstanding performances of the developed approach in sensitive and selective determination of H2O2 from human serum provides effective proof for its potential application in monitoring low-abundance H2O2 from complicated biological samples. This research not only expands the electrochemical applications of metalloporphyrin frameworks but also demonstrates a promising strategy for increasing the conductivity and stability of MOF-based electrode materials.

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