Subsequently, the RNA slot (containing the raw counts) of the merged object was used as an input for Harmony, as previously described (Korsunsky et al., 2019). genes that define the infected AM_2 subpopulation when compared with the rest of the infected cells in the dataset. JEM_20210615_TableS9.xlsx (23K) GUID:?98006ACB-B4E8-41CD-95C8-93298BD4DA7F Table S10: is a DGE results table comparing infected AM_2 versus AM_1 clusters. JEM_20210615_TableS10.xlsx (38K) GUID:?4D141B63-6FC3-494E-83FA-9DF3718DB3AF Table S11: is a DGE results table comparing infected AM_2 versus AM_3 clusters. JEM_20210615_TableS11.xlsx (28K) GUID:?1BFD1808-9D3E-41AD-8649-4AE7FB5A18FF Table S12: is a DGE results table comparing infected AM_2 versus AM_Pro-Infl clusters. JEM_20210615_TableS12.xlsx (55K) GUID:?36F6CAF5-DD64-482F-A3AF-ED0CF7168EFA Table S13: shows marker genes that define the uninfected AM_2 subpopulation when compared with the rest of the uninfected cells in the dataset. JEM_20210615_TableS13.xlsx (20K) GUID:?E788A918-A967-4C60-94F9-CE158B81D94B Table S14: is a DGE results table comparing uninfected AM_2 versus AM_3 clusters. JEM_20210615_TableS14.xlsx (23K) GUID:?ED8933AC-D36A-48B6-BF28-FAFC08D07ADB Table S15: is a DGE results table comparing uninfected AM_2 versus AM_1 clusters. JEM_20210615_TableS15.xlsx (17K) GUID:?61229747-EA7A-4E38-83E1-8FA6C3D604F9 Table S16: is a DGE results table comparing bystander AM_2 versus AM_1 clusters. JEM_20210615_TableS16.xlsx (30K) GUID:?B38094D3-429F-4C0D-9D91-CA6394052FC7 Table S17: is a DGE results table comparing bystander AM_2 versus AM_3 clusters. JEM_20210615_TableS17.xlsx (26K) GUID:?68A6A5CA-D113-4772-8ED0-38BABF7970D7 Table S18: shows marker genes that define the bystander AM_2 subpopulation when compared with the rest of the bystander cells in the dataset. JEM_20210615_TableS18.xlsx (39K) GUID:?DEAEE849-B80A-4B6F-B01C-72F070D1D30B Table S19: shows marker genes that define the AM_4 subpopulation across all cells, independently of infection status. JEM_20210615_TableS19.xlsx (33K) GUID:?9A946628-5F52-4A02-9339-E17792A5055A Table S20: shows marker genes that define the AM_1 subpopulation across all cells, independently of infection status. JEM_20210615_TableS20.xlsx (24K) GUID:?8C1D916E-90F2-4781-80FF-34AC7B01193F Table S21: shows marker genes that define the AM_3 subpopulation across all cells, independently of infection status. JEM_20210615_TableS21.xlsx (29K) GUID:?48343960-B622-47FE-B8A8-B7ABB9617F7C Table S22: is a DGE results table comparing Mtb (Mtb) remains the greatest cause of death by Cephalexin monohydrate a single Cephalexin monohydrate infectious agent and is calculated to have a penetrance extending to 23% of the human population (Houben and Dodd, 2016). In immune-competent individuals, the parameters that determine control or progression of disease remain extremely poorly defined. Macrophages represent the most significant infected host cell and were regarded as a homogenous, blood monocyteCderived cell lineage that was programmable by cytokines to adopt differing activation states (van Furth and Cohn, 1968). However, fate-mapping and cell-profiling studies have shown that macrophages resident in tissues, SLCO2A1 such as the lung and skin, arise from various stem cell Cephalexin monohydrate lineages during embryonic development (Gibbings et al., 2017; Ginhoux and Guilliams, 2016; Ginhoux and Jung, 2014), in addition to those cells recruited from the blood. To date, two main macrophage populations have been identified in the lung: tissue-resident alveolar macrophages (AMs) and monocyte-derived interstitial macrophages (IMs). Recent work, including our previous studies, has started to shed light on the role of these different macrophages lineages in Mtb infection in vivo (Huang et al., 2018), revealing that AMs constitute an anti-inflammatory M2-type population whose environment is favorable for Mtb replication and dissemination (Cohen et al., 2018; Huang et al., 2018; Pisu et al., 2020a), while IMs are associated with an immune milieu more stressful for the bacteria (Huang et al., 2018; Pisu et al., 2020a). However, these studies lack the ability to resolve the functional heterogeneity known to exist within these two main macrophage lineages (Chakarov et al., 2019; Evren et al., 2021; Liu et al., 2019). Building on the studies of Stoeckius et al. (2017), we developed a multimodal Cephalexin monohydrate approach to associate bacterial and host cell phenotypes at the single cell level. Using a bacterial reporter strain whose fluorescent expression correlates with the amount of environmental stress sensed by Mtb in each individual host cell (Abramovitch et al., 2011; Sukumar et al., 2014; Tan et al., 2013; Tan et al., 2017), we were able to simultaneously acquire the host transcriptome, surface markers expression, and the bacterial fitness phenotype. Through data integration (Korsunsky et al., 2019), we identified macrophage populations with common cell identities across different infection states. This enabled us to characterize those AM subsets that either restricted or promoted bacterial growth, in addition to defining a population of replicating tissue-resident AMs. We also identified three distinct populations of IMs: a population of monocyte-derived erythrophagocytic macrophages with high levels of expression that Cephalexin monohydrate induced drug tolerance in Mtb; a population of anti-inflammatory Nrf2-expressing IMs associated with bacteria sensing a low amount of environmental stress; and finally a population of bacillus CalmetteCGurin (BCG). These data suggest that much of the divergence of response between AMs and IMs is epigenetically controlled and actually precedes mycobacterial insult. The multimodal scRNA-seq approach detailed here is readily transferable to other.