Open Access

Fetal Liver–Derived Alveolar-like Macrophages: A Self-Replicating Ex Vivo Model of Alveolar Macrophages for Functional Genetic Studies

Sean T. Thomas, Kathryn A. Wierenga, James J. Pestka and Andrew J. Olive
ImmunoHorizons February 1, 2022, 6 (2) 156-169; DOI: https://doi.org/10.4049/immunohorizons.2200011

Abstract

Alveolar macrophages (AMs) are tissue-resident cells in the lungs derived from the fetal liver that maintain lung homeostasis and respond to inhaled stimuli. Although the importance of AMs is undisputed, they remain refractory to standard experimental approaches and high-throughput functional genetics, as they are challenging to isolate and rapidly lose AM properties in standard culture. This limitation hinders our understanding of key regulatory mechanisms that control AM maintenance and function. In this study, we describe the development of a new model, fetal liver–derived alveolar-like macrophages (FLAMs), which maintains cellular morphologies, expression profiles, and functional mechanisms similar to murine AMs. FLAMs combine treatment with two key cytokines for AM maintenance, GM-CSF and TGF-β. We leveraged the long-term stability of FLAMs to develop functional genetic tools using CRISPR-Cas9–mediated gene editing. Targeted editing confirmed the role of AM-specific gene Marco and the IL-1 receptor Il1r1 in modulating the AM response to crystalline silica. Furthermore, a genome-wide knockout library using FLAMs identified novel genes required for surface expression of the AM marker Siglec-F, most notably those related to the peroxisome. Taken together, our results suggest that FLAMs are a stable, self-replicating model of AM function that enables previously impossible global genetic approaches to define the underlying mechanisms of AM maintenance and function.

Introduction

Tissue-resident immune cells regulate homeostasis and control local inflammation to external stimuli. A subset of these immune cells are tissue resident macrophages (TRMs) that sample the environment and initiate host responses (1). Distinct TRM populations exist in specific tissues including the liver (Kupffer cells), the skin (Langerhans cells), the brain (microglia), and the lungs (alveolar macrophages [AMs]). These distinct TRMs all have unique functions that are regulated by the local environment and are required for tissue maintenance (2, 3).

As the first line of defense in the airways, AMs are particularly important for tuning the host immune response in the lungs (4). AMs can be distinguished from other macrophage populations in the lung by the surface expression of the sialic acid receptor Siglec-F, the scavenger receptor MARCO, and the integrin CD11c in addition to the high expression and activity of the transcription factor PPARγ, which drives many AM-specific genes (5, 6). AMs are a long-lived and self-replicating and, similar to most TRMs, are derived from embryonic precursors (7). AMs arise from fetal liver monocytes, which migrate to the lung and develop into mature AMs in the presence of cytokines such as GM-CSF and TGF-β shortly after birth (8–10). The continued presence of these factors is necessary for the maintenance and self-renewal of AMs in the lung, in part by promoting expression and activation of PPARγ (10, 11). Genes and pathways induced by this receptor are involved in lipid metabolism and induction of scavenger receptors that promote phagocytosis (12). This is critical for the AM roles of maintaining surfactant homeostasis, efferocytosis of cellular debris, and phagocytosis of inhaled microbes and particles in the alveolar space (12, 13). Impaired clearance of surfactant by AMs can result in the pathophysiological condition known as pulmonary alveolar proteinosis (14). In addition, reduced levels of AM efferocytosis and phagocytosis have been observed in patients with asthma, chronic obstructive pulmonary disease, and cystic fibrosis, likely contributing to the sustained inflammation and susceptibility to infection observed in these diseases (15–19).

Despite the paramount importance of AMs for lung health, there continue to be key gaps in our understanding of how they are maintained and function to regulate the host response in the lungs. One hurdle toward a mechanistic understanding of AMs is that experiments employing primary AMs require large numbers of animals to isolate a small number of cells that do not robustly proliferate or maintain AM-like functions ex vivo (20). This limitation has prevented genetic approaches from being employed to better understand AM maintenance and function. As a result, many ex vivo studies investigating responses to airborne particles and microbes rely on bone marrow–derived macrophages (BMDMs) or transformed macrophage cell lines as surrogates of AM biology (21–23). Although these macrophage models are useful, they do not faithfully recapitulate all AM functions (24–26). A recent alternative approach cultured cells from the murine fetal liver in the presence of GM-CSF to generate AM-like cells that are functionally and phenotypically similar to AMs (25). This approach enabled the isolation of large numbers of AM-like cells that might be amenable to tractable genetic approaches. However, we found in this study that fetal liver–derived cells cultured in GM-CSF alone lost their AM-like morphology, phenotype, and surface marker expression over time, suggesting that GM-CSF is insufficient to maintain the AM-like phenotype. A recent study found that AMs could be continuously cultured ex vivo in the presence of GM-CSF and TGF-β (27), which is consistent with reports that TGF-β promotes AM development and maintains AM function both in vivo and ex vivo (10).

In the present study, we found that growing fetal liver cells in both GM-CSF and TGF-β results in a long-term stable population of cells that are phenotypically and functionally similar to AMs. Using these fetal liver–derived alveolar-like macrophages (FLAMs), we developed targeted and global genetic tools to dissect regulatory networks that are required to maintain AM-like cells and function. Employing targeted gene editing, we show in the present study that directed mutations are readily introduced in FLAMs to query specific AM functions. We further demonstrate the utility of FLAMs by using a genome-wide CRISPR-Cas9 knockout screen to identify genes that are required for the surface expression of the AM-specific marker Siglec-F. The screen identified key pathways used to maintain Siglec-F expression and the AM-like state, including the observation that peroxisome biogenesis plays a central role in maintaining AM functions. Taken together, our results show that FLAMs enable the global dissection of AM regulatory mechanisms at a previously impossible scale.

Materials and Methods

Table of key reagents

See Supplemental Table I for a list of key reagents, catalog numbers, and oligonucleotide sequences.

Animals

Experimental protocols were approved by the Institutional Animal Care and Use Committee at Michigan State University (animal use form [AUF] no. PROTO201800113). Six- to 8-wk-old C57BL/6 mice (catalog no. 000664) and Cas9+ mice (catalog no. 026179) were obtained from The Jackson Laboratory (Bar Harbor, ME). Mice were given free access to food and water under controlled conditions (humidity, 40–55%; lighting, 12-h light/12-h dark cycles; and temperature, 24 ± 2°C), as described previously (28, 29). Pregnant dams at 8–10 wk of age and 14–18 gestational days were euthanized to obtain murine fetuses. AMs were isolated from male and female mice >10 wk of age. BMDMs were obtained from male and female mice ≥6 wk of age.

FLAM cell isolation and culture

Fetal liver–derived cells were obtained as previously described (25). Briefly, pregnant dams were euthanized by CO2 inhalation for 10 min to ensure death to neonates, which are resistant to anoxia. Cervical dislocation was used as a secondary form of death for the dam. Fetuses were immediately removed, and loss of maternal blood supply served as a secondary form of death for the fetuses. Cells were cultured in complete RPMI (Thermo Fisher Scientific) containing 10% FBS (R&D Systems), 1% penicillin-streptomycin (Thermo Fisher Scientific), 30 ng/ml recombinant mouse GM-CSF (PeproTech), and 20 ng/ml recombinant human TGF-β1 (PeproTech) included where indicated. Media were refreshed every 2–3 d. When cells reached 70–90% confluency, they were lifted by incubating for 10 min with 37°C PBS containing 10 mM EDTA, followed by gentle scraping. After ∼1 wk, adherent cells adopted a round, AM-like morphology. At this time, stocks were frozen for future use. Thawed stocks were plated in untreated petri dishes with either GM-CSF or GM-CSF and TGF-β and subcultured as described above.

AM isolation and culture

Mice were euthanized by CO2 exposure followed by exsanguination via the inferior vena cava. Lungs were lavaged as previously described (20). Cells were then resuspended in RPMI 1640 media containing 30 ng/ml GM-CSF and plated in untreated 48- or 24-well plates. AMs were lifted from plates using Accutase (BioLegend) and seeded for experiments.

BMDM isolation and culture

C57BL/6J mice were euthanized by CO2 exposure followed by cervical dislocation. Both femurs were cut on one end to expose the bone marrow, placed cut side down in 0.6-ml tubes, and centrifuged at 16,000 × g for 25 s. Marrow from multiple mice was pooled, dissociated to a single-cell suspension in sterile PBS, and pelleted by centrifuging at 220 × g for 5 min. The pellet was resuspended in mouse RBC lysis buffer (Alfa Aesar) and incubated at room temperature for 5 min. The RBC lysis buffer was diluted with 2 vol of PBS and the cell suspension was passed through a nylon 70-μm filter (Corning Life Sciences). Cells were pelleted a second time and resuspended in RPMI 1640 media containing 10% FBS, 1% penicillin-streptomycin, and 20% L929 media (30). Approximately 5 × 106 cells were plated per dish in 10-cm untreated petri dishes. Media were refreshed every 2–3 d. Cells were used for assays when fully differentiated after 7 d.

Flow cytometry

Plated cells were lifted in warm PBS with 10 mM EDTA for 5–10 min and washed twice in PBS before fluorescent Ab labeling. Immediately following isolation, AMs were resuspended in PBS and filtered through a 70-µm basket filter and incubated with an Ab mixture of PE CD170, allophycocyanin CD11c, allophycocyanin-Cyanine7 CD14, and FC Block (BioLegend; 1:400 in PBS) for 20 min at room temperature in light-free condition. Immunochemically labeled cells were washed three times with PBS, resuspended in PBS, and passed through a 70-μm nylon filter immediately prior to analysis. Flow cytometry was performed on an LSR II flow cytometer (BD Biosciences) at the Michigan State University Flow Cytometry Core.

qPCR

RNA was isolated from ∼5 × 105 cells using RNeasy mini kits (Qiagen), typically yielding 100–400 ng of RNA. RNA was then reverse transcribed to cDNA using a high-capacity cDNA reverse transcription kit (Thermo Fisher Scientific) on a Stratagene Robocycler 40. Quantitative real-time PCR was performed using specific TaqMan probes (Thermo Fisher Scientific) for TGF-β1 (Tgfb1), TGF-β receptors (Tgfbr1, Tgrbr1), selected genes used to distinguish AMs from other macrophage populations (Cd14, Siglecf, Marco, Pparg, Car4, Fabp4, Itgax), and cytokines (Il1a) on an Applied Biosystems QuantStudio 7 real-time PCR system. Data were analyzed with Applied Biosystems Thermo Fisher Cloud using the RQ software and the relative quantification method. Gapdh was used as the housekeeping gene. Relative copy number for each gene was normalized to expression of Gapdh and calculated as described previously (31).

Scanning electron microscopy

Suspensions of AMs or FLAMs were diluted to 2.5 × 105 cells/ml, and 100 μl was pipetted directly upon 12-mm-diameter, 0.13- to 0.16-mm-thick glass circular coverslips (Electron Microscopy Sciences), which were placed in the bottom of six-well plates. Cells were allowed to settle for 2–3 min, and then 1 ml of media was added to fill the well. To fix cells, the coverslips were removed from the wells, submerged in 4% glutaraldehyde in 0.1 M sodium phosphate buffer at pH 7.4 and placed in a graded ethanol series (25, 50, 75, 95%) for 10 min at each step followed by 3-min changes in 100% ethanol.

Samples were critical point dried in a Leica Microsystems model EM CPD300 critical point drier (Leica Microsystems, Vienna, Austria) using CO2 as the transitional fluid. Coverslips were then mounted on aluminum stubs using epoxy glue (System Three Quick Cure 5, System Three Resins, Auburn WA). Samples were coated with osmium at ∼10-nm thickness in an NEOC-AT osmium chemical vapor deposition coater (Meiwafosis, Osaka, Japan) and examined in a JEOL 7500F (field emission emitter) scanning electron microscope (JEOL, Tokyo, Japan).

cSiO2 phagocytosis assay

To assess phagocytosis of crystalline silica (cSiO2) particles, FLAMs, AMs, and BMDMs were seeded at 0.25 cells/cm2 in 48- or 96-well plates to observe engulfment of surrounding silica particles. The following day, the media were removed, wells were rinsed once with sterile PBS, and media were replaced with FluoroBrite DMEM (Thermo Fisher Scientific) containing 10% FBS and 200 nM Sytox Green nucleic acid stain (Thermo Fisher Scientific). cSiO2 was then added dropwise to a final density of 25–100 μg/cm2. Cells were imaged over time on an EVOS FL2 fluorescence microscope (Thermo Fisher Scientific) with an on-stage, temperature control CO2 incubator, and two to four images were acquired per well. Sytox Green was detected on the GFP light cube.

Images were analyzed using analysis pipelines built in the CellProfiler software (32). cSiO2 engulfment was assessed by quantifying the number of cSiO2-filled cells, which have a higher pixel intensity than non-cSiO2–filled cells due to the accumulation of the particles. To avoid counting aggregated cSiO2 particles, a threshold was applied to capture only shapes with high solidity and low compactness. Cell death was quantified by counting Sytox Green+ cells.

ELISAs

Cells were treated with cSiO2 for 8 h or LPS for 24 h at the indicated concentrations. Cell-free supernatant was collected and the cytokines IL-1α, IL-1β, and IL-10 were analyzed using DuoSet ELISA kits (R&D Systems) per the manufacturer’s instructions.

CRISPR-targeted knockouts

Single-guide RNA (sgRNA) cloning sgOpti was a gift from Eric Lander and David Sabatini (Addgene plasmid no. 85681) (33). Individual sgRNAs were cloned as previously described (34). In short, sgRNA targeting sequences were annealed and phosphorylated, then cloned into a dephosphorylated and BsmBI (New England Biolabs) digested sgOpti. sgRNA constructs were then packaged into lentivirus as previously described and used to transduce early passage FLAMs. Two days later, transductants were selected with puromycin. After 1 wk of selection, genomic DNA was isolated from each targeted FLAM, and PCR was used to amplify edited regions and Sanger sequencing was used to quantify indels. Two sgRNAs were targeted per gene, and one targeted line was selected for a follow-up study with editing efficiency >98% for each gene.

Construction of genome-wide loss-of-function library and Siglec-F screen

The mouse BRIE knockout CRISPR pooled library was a gift of David Root and John Doench (Addgene no. 73633) (35). Using the BRIE library, four sgRNAs targeting every coding gene in mice in addition to 1000 non-targeting controls (78,637 sgRNAs total) were packaged into lentivirus using HEK293T cells and transduced Cas9+ FLAMs at a low multiplicity of infection (<0.3). Two days later these cells were selected with puromycin. We then passaged the transduced library in TGF-β in parallel with non-transduced cells of the same passage without TGF-β. When the non-transduced cells grown in the absence of TGF-β showed reduced Siglec-F expression by flow cytometry, we isolated genomic DNA from the library for sequencing and found high coverage and distribution, with only 1000 sgRNAs not found in the input library. In parallel, the transduced library was fixed, and FACS was used to isolate the Siglec-Fhigh and Siglec-Flow bins using a Bio-Rad S3e cell sorter. Genomic DNA was isolated from each sorted population from two biological replicate experiments using a homemade modified salt precipitation method previously described (36). Amplification of sgRNAs by PCR was performed as previously described using Illumina-compatible primers from IDT (35), and amplicons were sequenced on an Illumina NovaSeq 6000 at the Research Technology Support Facility Genomics Core at Michigan State University.

Sequence reads were first trimmed to remove any adapter sequence and to adjust for p5 primer stagger. We used model-based analysis of genome-wide CRISPR-Cas9 knockout (MAGeCK) to map reads to the sgRNA library index without allowing for any mismatch. Subsequent sgRNA counts were median normalized to control sgRNAs in MAGeCK to account for variable sequencing depth. To test for sgRNA and gene enrichment, we used the “test” command in MAGeCK to compare the distribution of sgRNAs in the Siglec-Fhigh and Siglec-Flow bins.

Bioinformatics analysis

Both DAVID analysis and gene set enrichment analysis (GSEA) were used to identify enriched pathways and protein families that were enriched in the dataset. Genes were ranked in MAGeCK using robust rank aggregation (RRA), and the top enriched positive regulators (4-fold change with at least two sgRNAs) were used as a “candidate list” in DAVID analysis using default settings (37). Functional analysis and functional annotation analysis were completed, and top enriched pathways and protein families were identified. For GSEA analysis, the “GSEA Preranked” function was used to complete functional enrichment using default settings for Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and Gene Ontology terms.

Data availability

Raw sequencing data in FASTQ and processed formats are available for download from National Center for Biotechnology Information’s Gene Expression Omnibus under the accession number GSE195868 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE195868).

Statistical analysis and data visualization

Statistical analysis and data visualization were performed using Prism version 8 (GraphPad Software), Biorender, or R studio as indicated in the figure legends. Sytox+ and cSiO2-filled cells were quantified using CellProfiler. Data are presented, unless otherwise indicated, as the mean ± SD. For parametric data, one-way ANOVA followed by a Tukey post hoc test was used to identify significant differences between multiple groups, and Student t tests were used to compare two groups. Nonparametric one-way ANOVAs and Mann–Whitney U tests were used to compare multiple groups and two groups, respectively, for nonparametric data.

Results

Fetal liver–derived cells and AMs cultured in GM-CSF alone do not stably express lineage-specific markers over time

The development of a genetically tractable AM ex vivo model requires a stable population that maintains AM-like phenotypes and functions long-term. As a first step, we examined the long-term stability of AM-like cells using a previously described method culturing fetal liver–derived cells in the cytokine GM-CSF (25). Consistent with previous reports, we found that fetal liver cells grown in GM-CSF phenotypically and morphologically resemble AMs (25, 38, 39). Fetal liver cells grown for 2 wk ex vivo in the presence of GM-CSF adopt a distinct fried egg–like morphology akin to AMs (Fig. 1A). Scanning electron microscopy revealed that the surfaces of both AMs and low-passage fetal liver cells (<1 mo of culture) have numerous outer membrane ruffles (Fig. 1B). However, high-passage fetal liver cells (>1 mo of culture) underwent a morphological shift from an AM-like, ovoid morphology with numerous outer plasma membrane ruffles, to a smaller, fusiform morphology with loss of membrane ruffles (Fig. 1A, 1B). Thus, in our hands, the morphology of fetal liver–derived cells grown in recombinant GM-CSF are not stable long-term.

FIGURE 1.
FIGURE 1.

Fetal liver macrophages cultured with GM-CSF lose their AM-like phenotype over time.

Fetal liver cells were cultured with GM-CSF and analyzed at indicated passage. AMs were isolated and analyzed immediately. (A) AMs, passage (P) 2 fetal liver cells, and P37 fetal liver cells were lifted from culture and imaged on a EVOS FL Auto 2 fluorescence microscope at ×60 original magnification. (B) AMs, P3 fetal liver cells, and P15 fetal liver cells were fixed and imaged by scanning electron microscopy at ×4500, ×4000, and ×2200, respectively. (C) AMs (gray), P4 fetal liver macrophages (blue), and P15 fetal liver macrophages (red) were assessed for surface expression of the markers CD14, Siglec-F, and CD11c by flow cytometry. (D) Gene expression of indicated genes in AMs, early fetal liver macrophages (P1), and late fetal liver macrophages (P18) was quantified by qPCR. Data were compared using one-way ANOVA followed by a Tukey multiple comparison test. Bars labeled with unique letters are significantly different (p < 0.05). Results are representative of two or three independent experiments.

We further examined whether changes in surface markers or gene expression varied as fetal liver cells were cultured over time. Using flow cytometry, we found similarity between AMs and low-passage fetal liver cells with high surface expression of Siglec-F and CD11c and low expression of CD14. However, high-passage fetal liver cells showed low expression of Siglec-F and CD11c while expressing high levels of CD14 (Fig. 1C). Similarly, when we quantified gene expression, we observed that low-passage fetal liver cells and AMs express high levels of Pparg, Car4, Il1a, and Fabp4 (a transcriptional target of PPARγ) and low levels of CD14, whereas high-passage fetal liver cells expressed very low levels of the AM-associated transcripts but high levels of CD14 (Fig. 1D). We observed similar results with AMs isolated from the lungs (Supplemental Fig. 1A, 1B). To exclude the possibility of contaminating cells outcompeting the alveolar-like cells over long-term culture, we used FACS to isolate a pure Siglec-F+ population of cells that were then cultured in GM-CSF media. We continued to observe a decline in Siglec-F and CD11c in these cells (Supplemental Fig. 1C). Taken together, these data suggest that prolonged culture of fetal liver–derived cells in GM-CSF media results in a decline in AM-like properties.

Fetal liver–derived cells grown in GM-CSF and TGF-β are phenotypically similar to AMs long-term

We next pursued strategies to improve the stability of AM-specific phenotypes of fetal liver–derived cells grown ex vivo. Based on a prior report that the cytokine TGF-β is critical to AM development and homeostasis (10), we hypothesized that the addition of TGF-β to our culture system would maintain cells in an AM-like state. We first tested whether the addition of TFG-β alters the expression of AM-associated genes. Fetal liver cells in GM-CSF media were treated for 24 h with 10 ng/ml TGF-β, which we found induced the genes Pparg, Car4, and Itgax (Supple-mental Fig. 2A), all of which are highly expressed by AMs (Fig. 1D). We next examined whether continued supplementation with TGF-β stabilizes the AM-like phenotypes of fetal liver cells long-term. Fetal liver–derived cells were cultured in GM-CSF media or GM-CSF media containing 20 ng/ml TGF-β. After 15 passages (∼2 mo of culturing), cells grown in the presence of TGF-β retained a round, AM-like morphology (Fig. 2A) and continued expressing AM-identifying genes (Fig. 2B). Conversely, fetal liver–derived cells cultured without TGF-β lost the expression of AM-identifying genes Siglecf, Marco, and Pparg and began expressing Cd14, which is a common marker for monocyte-derived macrophages recruited to the lung (40) (Fig. 2B).

FIGURE 2.
FIGURE 2.

Culturing fetal liver cells with TGF-β and GM-CSF maintains AM-like phenotypes long-term.

Fetal liver cells were cultured with or without TGF-β for the indicated passages. (A) Passage (P) 15 cells cultured with and without TGF-β were imaged on a EVOS FL Auto 2 fluorescence microscope at ×60 original magnification. Cells with a clearly visible spindleoid morphology are marked with arrows. (B) At the indicated passage, RNA was extracted from a subset of cells for gene expression analysis. Expression of the indicated genes are quantified as relative copy number (RCN) compared with Gapdh. Asterisks indicate significant differences in gene expression of cells cultured with and without TGF-β cells at the same passage number, as determined by a Student t test. *p < 0.05. (C) At the indicated passage, cells were analyzed by flow cytometry for the surface expression of CD14 and Siglec-F. Representative biaxial plots from triplicate samples are shown. (D) Quantification of the mean fluorescence intensity (MFI) and percent of cells positive of CD14 and Siglec-F surface expression from cells in (C) expressed as MFI (left) and percent positive (right). Results are representative of at least two independent experiments. ****p < 0.0001 by one-way ANOVA with a Tukey correction for multiple comparisons.

We next determined whether fetal liver–derived cells grown in the absence of TGF-β would revert to the AM-like state upon the addition of TGF-β. Fetal liver cells grown in the absence of TGF-β were cultured with and without TGF-β for 6 d, and the expression levels of Siglec-F and CD14 were quantified by flow cytometry (Supplemental Fig. 2B). In parallel, fetal liver cells maintained in TGF-β were cultured for 6 d in the presence or absence of TGF-β. We observed that whereas the removal of TGF-β resulted in a significant decrease in Siglec-F and an increase in CD14, there was no change in expression upon the addition of TGF-β to fetal liver cells that previously lost AM-like marker expression. When we examined the gene expression of Tgfb1 and the TGF-β receptors, Tgfbr1 and Tgfbr2, we observed a significant decrease in expression of the Tgfb1 (Fig. 2B, Supplemental Fig. 2C). These data suggest that loss of the AM-like potential of fetal liver cells grown in the absence of TGF-β is not reversible.

We next quantified changes in the surface expression of Siglec-F and CD14 by flow cytometry in the fetal liver–derived cells grown in the presence and absence of TGF-β. Consistent with our gene expression analysis we observed that fetal liver–derived cells lose the expression of Siglec-F and gain the expression of CD14 over time (Fig. 2C, 2D). In contrast, fetal liver–derived cells grown with TGF-β maintained >80% of cells with high levels of Siglec-F expression and low levels of CD14. Thus, culturing fetal liver cells in both GM-CSF and TGF-β results in the stable gene expression of self-replicating cells that phenotypically resemble AMs.

Fetal liver–derived cells grown in GM-CSF and TGF-β are functionally similar to AMs in response to cSiO2 relative to phagocytosis, IL-1 cytokine release, and death

To assess the functional similarity of fetal liver–derived cells grown in TGF-β with AMs, we assessed the response of cells to cSiO2, a respirable particle associated with silicosis and autoimmunity (41, 42). Cells were exposed to various concentrations of cSiO2 for 8 h. Sytox Green, a membrane-impermeable nucleic acid stain, was included to assess lytic cell death. AMs and low-passage fetal liver–derived cells grown with and without TGF-β showed similar rates of cSiO2 engulfment and cell death (Fig. 3A). However, late-passage fetal liver–derived cells without TGF-β exhibited poor phagocytosis, and, as a result, tolerated the presence of cSiO2 without inducing cell death. This unresponsiveness is prevented by TGF-β, as late-passage fetal liver–derived cells effectively phagocytosed cSiO2 (Fig. 3A, 3B). Rates of phagocytosis by BMDMs were comparable to AMs but were accompanied by a 2-fold increase in cell death. Thus, fetal liver–derived cells grown in TGF-β and GM-CSF are functionally stable long-term and recapitulate phagocytosis and cell death kinetics similarly to AMs.

FIGURE 3.
FIGURE 3.

The kinetics of cSiO2 uptake and cSiO2-induced cell death and IL-1 release are similar among AMs and FLAMs.

AMs, bone marrow-derived macrophages (BMDMs), and fetal liver cells were seeded in 96-well plates. After 24 h, media were replaced with FluoroBrite DMEM containing 200 nM Sytox green and 10% FBS. cSiO2 at the indicated densities was added dropwise to cells and images were taken at 0, 2, 6, and 8 h using an EVOS FL2 fluorescence microscope. (A) The percentages of cSiO2-filled and Sytox+ cells were quantified using CellProfiler software. (B) Representative images of Sytox+ and cSiO2-filled cells (white arrows in AM panel, top right) treated with 50 μg/cm2 silica for 8 h; original magnification, ×20. (C) In a separate experiment, the supernatant was collected after an 8-h treatment with 25 μg/cm2 to assess release of the cytokines IL-1α (top) and IL-1β (bottom) by ELISA. **p < 0.01, ***p < 0.001, as assessed by Student t tests between relevant groups. ND, not detected. Results are representative of at least two independent experiments.

IL-1α is associated with the inflammatory response to particle-induced inflammation. In vivo and ex vivo studies suggest that AMs are the primary source of IL-1α in the lung following inhalation of cSiO2, likely as a result of cell death (43, 44). Initial characterizations of fetal liver–derived cells grown in GM-CSF (25) showed they respond to LPS such as AMs by producing high levels of IL-1α and low levels of IL-10 in contrast BMDMs that make little IL-1α. We replicated these experiments and observed similar results with low-passage fetal liver–derived cells producing high levels of IL-1α and low levels of IL-10 in response to LPS (Supplemental Fig. 2D). We next tested how the IL-1α response to cSiO2 differed over time in fetal liver–derived cells grown in the presence and absence of TGF-β. We found that high levels of IL-1α were released in both low- and high-passage fetal liver–derived cells grown in both GM-CSF and TGF-β following cSiO2 exposure for 8 h, similar to AMs (Fig. 3C). In contrast, we observed that cSiO2 induced IL-1α release from low-passage fetal liver–derived cells grown in GM-CSF alone but not late passage cells. Late-passage cells grown in GM-CSF alone instead phenocopied BMDMs and released no detectable IL-1α following cSiO2 exposure. Release of IL-1β in these cells may be indicative of inflammasome activation, which is a major mechanism of AM toxicity following exposure to cSiO2. We found cSiO2 exposure to elicit modest IL-1β release from low-passage fetal liver–derived cells grown without TGF-β, and from both low- and high-passage fetal liver–derived cells grown with TGF-β. We further observed a slight, although not significant, increase in IL-1β release in AMs following cSiO2 exposure (Fig. 3C). cSiO2-induced IL-1β release was not evident from BMDMs or late MPI cells. Taken together, these experiments show that growth of fetal liver cells in both GM-CSF and TGF-β recapitulates many aspects of AM physiology and function as stable, long-term, self-propagating cells. We call these cells FLAMs.

CRISPR-Cas9 editing in FLAMs enables disruption of AM-specific responses to cSiO2

A significant hindrance in the study of AMs is their intractability to standard genetic approaches. This shortcoming has limited the understanding of pathways and regulators that control AM maintenance and function. We hypothesized that FLAMs could be leveraged to dissect AM functional mechanisms. To test this hypothesis we developed CRISPR-Cas9–mediated gene-editing tools by generating FLAMs from Cas9+ mice (45, 46). Using these cells, we targeted Marco and Il1r1, two genes associated with phagocytosis and inflammatory responses to cSiO2 in AMs (23, 47). Each gene was targeted using two independent sgRNAs per gene by lentiviral transduction. Following selection of successfully transduced cells, we evaluated the editing efficiency of each target genes using tracking of indels by decomposition (TIDE) analysis. We observed robust editing for both sgRNAs with at least one sgRNA per gene reaching >95% editing efficiency (see Materials and Methods). Thus, FLAMs are amenable to genetic targeting by CRISPR-Cas9.

Given that the scavenger receptor MARCO has been shown to be involved in cSiO2 uptake and toxicity and that IL-1R1 is known to amplify inflammatory cues, we hypothesized that cells deficient in MARCO and IL-1R1 expression would have a reduced inflammatory response to cSiO2 (47, 48). We therefore tested whether FLAMs targeted for Marco or Il1r1 would differentially respond to cSiO2 exposure compared with wild-type. We exposed control FLAMs and sgMarco or sgIl1r1 FLAMs to two different cSiO2 concentrations and quantified cell death. Although we observed no change in cell death in sgIl1r1 FLAMs compared with control FLAMs, a significant reduction in cell death in sgMarco FLAMs was observed following high cSiO2 exposure (Fig. 4A). We next examined the production of IL-1 following exposure of cells to cSiO2. We found reduced cSiO2-induced IL-1α and IL-1β production by sgMarco and sgIl1r1 FLAMs compared with control FLAMs (Fig. 4B, 4C). Therefore, FLAMs are genetically tractable and can be used to dissect AM-specific functions.

FIGURE 4.
FIGURE 4.

The loss of Marco and Il1r1 modulate the response of FLAMs to cSiO2 treatment.

Wild-type, Marco knockout (KO), and Il1r1 KO FLAMs were treated with cSiO2 at the indicated concentrations for 8 h. (A) Cell viability was determined using the MTS assay, with 100% viability determined as the mean absorbance of the formazan dye product in the untreated wild-type cells. (B and C) Supernatant was collected to measure release of (B) IL-1α and (C) IL-1β. Results are representative of at least two independent experiments with biological triplicates. *p < 0.05, ***p < 0.001, between cell types within treatment groups, as determined by one-way ANOVA followed by a Tukey multiple comparison test.

Forward genetic screen in FLAMs identifies regulators of the AM surface marker Siglec-F

The genetic tractability of FLAMs opens the possibility of performing forward genetic screens in an AM context, which was previously unviable. We recently developed a screening platform in immortalized BMDMs (iBMDMs) that uses cell sorting of CRISPR-Cas9–targeted cells to enrich for genes that positively or negatively regulate the surface expression of important immune molecules (49). We hypothesized that this screening pipeline could be leveraged to dissect pathways responsible for the unique expression profiles seen in AMs and FLAMs. As a first step to test this hypothesis, we dissected the changes in the surface expression of Siglec-F when targeted using CRISPR-Cas9. Among macrophages, Siglec-F is uniquely expressed on the surface of AMs, yet how Siglec-F is regulated remains entirely unknown. Given that Siglec-F expression is lost as cells lose their AM-like phenotypes, globally understanding Siglec-F regulation in FLAMs may inform key gene networks in AMs. To test the dynamic range of Siglec-F expression on FLAMs, we targeted Siglec-F with two independent sgRNAs in both Cas9+ FLAMs and iBMDMs. Again, extensive editing for both sgRNAs was observed with one sgRNA reaching >99% editing efficiency. As expected, control iBMDMs showed no surface Siglec-F expression, and targeting Siglec-F showed no observable change by flow cytometry (Fig. 5A). In contrast, we observed robust Siglec-F expression on control FLAMs whereas sgSiglecF FLAMs showed a >100-fold reduction in mean fluorescence intensity (MFI) (Fig. 5B). This dynamic range is comparable to other surface markers we previously screened in iBMDMs, suggesting that Siglec-F is an ideal target for a genetic screen in FLAMs (49).

FIGURE 5.
FIGURE 5.

A loss-of-function forward genetic screen identifies regulators of Siglec-F surface expression on FLAMs.

iBMDMs or FLAMs targeted for Il1r1 or SiglecF were analyzed by flow cytometry for Siglec-F surface expression. (A) Shown are representative histograms of surface expression. (B) The MFI of Siglec-F surface expression was quantified on cells of the indicated genotypes. ****p < 0.0001, between samples by one-way ANOVA with a Tukey correction. These data are representative of two independent experiments. (C) Shown is a schematic of the generation of the FLAM knockout library and screen to identify Siglec-F regulators. Transduction of Cas9+ FLAMs with the genome-wide library of sgRNAs results in variable Siglec-F surface expression. When parallel control FLAMs grown in the absence of TGF-β lost Siglec-F expression, the top and bottom 5% of Siglec-F expression cells were isolated from the knockout FLAM library by FACS. Sorted cells were then used for downstream sequencing and analysis. (D) Siglec-F surface expression of library control cells grown in the absence of TGF-β was monitored over time and compared with the transduced FLAM library prior to sorting. Shown is the MFI for Siglec-F expression of the indicated cells and passage numbers. ****p < 0.0001 by one-way ANOVA with Tukey test. (E) Shown is the α-RRA score of each gene in CRISPR library that passed filtering metrics in MAGeCK. Genes of interest are noted. (F) Normalized sgSiglecF counts for each sgRNA found in both the Siglec-Flow– and Siglec-Fhigh–sorted populations is shown.

To globally identify genes that contribute to Siglec-F surface expression on FLAMs, we generated a genome-wide knockout library. FLAMs from Cas9+ mice were transduced with sgRNAs from the pooled Brie library (50), which contains four independent sgRNAs per mouse coding gene. In parallel to the library, we grew control Cas9+ fetal liver cells with GM-CSF alone to monitor the loss of Siglec-F expression in the absence of TGF-β signaling (Fig. 5C, 5D). When control cells lost Siglec-F expression, genomic DNA from the FLAMs knockout library was purified and the sgRNAs were quantified by deep sequencing. The library coverage was confirmed to have minimal skew. We then conducted a forward genetic screen using FACS to isolate the Siglec-Fhigh and Siglec-Flow cells from the loss-of-function FLAM library (Fig. 5C). Following genomic DNA extraction, sgRNA abundances for each sorted population were determined by deep sequencing. To test for statistical enrichment of sgRNAs and genes, we used the modified RRA (α-RRA) employed by MAGeCK. MAGeCK first ranks sgRNAs by effect and then filters low-ranking sgRNAs to improve gene significance testing (51). To identify genes that are required for Siglec-F expression we compared the enrichment of sgRNAs in the Siglec-Flow population to the Siglec-Fhigh population. The α-RRA analysis identified >300 genes with a p value <0.01 and the second ranked gene in this analysis was the target of the screen Siglec-F (Fig. 5E, Supplemental Tables II, III). Guide-level analysis showed agreement with all four sgRNAs targeting Siglec-F, with each showing a 10-fold enrichment in the Siglec-Flow population (Fig. 5F, Supplemental Table I). The high ranking of Siglec-F gives high confidence in genome-wide screen results.

Stringent analysis revealed an enrichment of genes with no previously described role in Siglec-F regulation including the TGF-β response regulator USP9x (52) (Fig. 6A). To identify pathways that were associated among these genes, we filtered the ranked list to include genes that had a fold change of >4 with at least two out of four sgRNAs and used DAVID analysis to identify pathways and functions that were enriched in our datasets. The top enriched KEGG pathway was the peroxisome, with all core components of peroxisome biogenesis identified as positive regulators of Siglec-F (Fig. 6B). We further examined other peroxisome-associated genes and found that 11 out of 15 peroxisome-associated genes present in our library were altered >2-fold (Fig. 6C). KEGG pathway analysis identified a significant enrichment in genes associated with lipid metabolism, including glycerophospholipid, inositol phosphate, and ether lipids. KEGG analysis also found an enrichment of the phagosome pathway that identified several surface receptors associated with phagocytosis in this pathway, including the IgG Fc receptor 4, the mannose-6-phosphate receptor, and the oxidized low-density lipoprotein receptor, suggesting that surface proteins associated with phagocytosis directly modulate the stability of Siglec-F (Fig. 6D). Examination of enriched UniProt keyword terms using DAVID analysis found a strong enrichment of proteins with oxidoreductase function, including several genes associated with cytochrome P450 (CYP), a key regulator of xenobiotic, fatty acid, and hormone metabolism, known to be important in the lung environment (53). Thus, bioinformatics analysis of the top positive regulators of Siglec-F identified pathways that are associated with AM functions.

FIGURE 6.
FIGURE 6.

Bioinformatics analysis identifies FLAM metabolic networks as critical regulators of Siglec-F expression.

(A) The TGF-β response regulator USP9x was a significant hit in the screen. Shown are the normalized counts for each of the four sgRNAs targeting USP9x in each sorted population. (B) Using DAVID analysis, peroxisome biogenesis was identified as the most significantly enriched KEGG pathway. Shown is an adaptation of the KEGG peroxisome biogenesis pathway highlighting the 10 peroxisome regulators identified in the screen in pink. (C) The sgRNA distribution and mean log fold change for each peroxisome regulator (Pex) identified in the genetic screen are shown. The dashed line indicates a log2 fold change of −1. (D) DAVID analysis identified surface proteins associated with phagocytosis. Shown are the normalized counts for each of the four sgRNAs targeting the indicated surface protein from each sorted population. (E) GSEA was used to identify enriched pathways from the entire forward genetic screen. Shown are four leading-edge analysis plots that are representative of this analysis for a subset of enriched pathways. These pathways include the peroxisome, oxidative phosphorylation, GPI anchor biosynthesis, and mTORC1 signaling.

We next used GSEA to identify functional enrichments from the entire ranked screen dataset. GSEA identified the peroxisome as a top enriched KEGG pathway, consistent with the DAVID analysis (Fig. 6E). This analysis also identified a strong enrichment for oxidative phosphorylation, which is consistent with the key metabolic changes in AMs compared with BMDMs (54), and a significant enrichment for GPI anchor synthesis as negative regulators of Siglec-F surface expression (Fig. 6E). We also noted that mTORC1 signaling was enriched as a positive regulator, in line with previous reports that mTORC1 is required to maintain AMs in the lungs (55). Taken together, our forward genetic screen not only identified Siglec-F, the screen target, but also identified positive and negative regulators of Siglec-F expression that are associated with known AM functions as well as novel AM regulators. Thus, FLAMs are a tractable genetic platform that enables the detailed interrogation of AM regulatory functions and mechanisms.

Discussion

As long-lived resident macrophages in the lungs, AMs have unique phenotypes and functions shaped by the alveolar environment (56). However, experimental limitations hinder our understanding of AM-specific functional mechanisms. Developing ex vivo models that recapitulate AM phenotypes would overcome the challenges associated with isolating and maintaining AMs from the lungs of mice. Because AMs are derived from fetal liver monocytes, previous studies tested the culture of fetal liver cells with GM-CSF (57). These culture conditions result in self-replicating AM-like cells, but in our hands, the AM-like phenotype was not stable long-term. Although low-passage fetal liver cells grown in GM-CSF are useful for some experimental approaches, the instability of the AM phenotype precludes functional genetic studies (27, 58). To stabilize the AM-like phenotype of fetal liver–derived cells, we supplemented the growth media with TGF-β, a key cytokine for AM maintenance in the lungs, in a model we term FLAMs (10). In this study, we showed that FLAMs recapitulate many aspects of AM biology, are stable long-term, and are genetically tractable, making them a useful tool to dissect the regulation of AM maintenance and function.

We demonstrated that even after 1 mo of culture, FLAMs efficiently phagocytose cSiO2 particles, produce inflammatory cytokines such as IL-1α, and die similarly to AMs. Our results are consistent with two recent reports that examined how TGF-β modulates macrophages ex vivo (10, 27, 58). These reports showed that TGF-β can induce/maintain AM-like phenotypes ex vivo using AMs directly from the lungs of mice or purified cells from the bone marrow. Future studies are needed to directly compare how distinct sources of AM-like cells grown in GM-CSF and TGF-β are functionally similar or distinct. There are other key differences in these approaches beyond the source of cells, including the use of the PPARγ agonist rosiglitazone. Our data strongly suggest that fetal liver cells do not require rosiglitazone to maintain PPARγ activity. Other advantages of FLAMs are the low cost, low technology threshold, and high yield of cells that can be isolated from any genetically modified mouse, including mice with embryonic lethality. Another key advantage of FLAMs is genetic toolbox that we have developed in the present study. Using targeted gene editing we showed that directed mutations can be easily generated in FLAMs to probe specific AM functions. Furthermore, we generated a genome-wide knockout library in FLAMs and completed the first forward genetic screen in AM-like cells. This proof-of-concept genome-wide screen in FLAMs now enables our innovative tools to be broadly used to understand AM biology in previously impossible detail. Thus, FLAMs recapitulate ex vivo AMs even after extended culturing and are suitable for dissecting AM responses and regulation.

How AMs control their functional responses and how this differs from other macrophage populations remain unclear. In the present study, we observed both phenotypic and functional differences among AMs, FLAMs, and BMDMs in line with previous studies (25, 38, 39). Whereas BMDMs express high levels of CD14, AMs and FLAMs express high levels of Siglec-F and MARCO. When cells were exposed to cSiO2, we observed differences in cell death kinetics and IL-1 cytokine responses. Although BMDMs were able to engulf cSiO2 particles at a rate comparable to AMs and FLAMs, they quickly succumbed to cell death, whereas AMs and FLAMs remained viable many hours following cSiO2 phagocytosis. A delay in cell death may be important for appropriate clearance of particles, potentially allowing the AMs to be transported out of the alveoli before they die (59). AMs and FLAMs also released significantly more IL-1α and IL-1β than did BMDMs in response to cSiO2. These data are consistent with studies showing high levels of IL-1α produced by AMs compared with other cells in the lung and other macrophage subtypes (25, 38, 43, 44, 57) and the known role of cSiO2 in inducing IL-1β release (23). Our findings indicate that MARCO may be a key player in driving the IL-1 cytokine response in AMs, as MARCO-deficient FLAMs showed increased viability and decreased IL-1 production following cSiO2 exposure. These results are in line with previous studies implicating MARCO in the uptake of cSiO2 and other particles in AMs (48, 60). In the future, FLAMs will be used to dissect the underlying mechanisms of MARCO regulation to understand how MARCO drives distinct inflammatory responses following phagocytosis of cSiO2 and other pathogenic cargo. Knocking out the IL-1 receptor also reduced IL-1 cytokine release, which points to a feed-forward mechanism to amplify this inflammatory response in AMs.

In addition to MARCO, AMs express other markers that are used to define AM populations. However, the regulation of these other AM markers, such as Siglec-F, remains entirely unknown. Siglec-F is a surface-expressed Ig protein that binds sialic acid residues on glycolipids and glycoproteins, but its function in AMs is largely unknown. In addition to AMs, Siglec-F is expressed on eosinophils, where it limits inflammation by modulating cell death pathways (61). The only studies examining Siglec-F in AMs demonstrated that Siglec-F does not regulate phagocytic activity (62). Our forward genetic screen in FLAMs defined regulators of Siglec-F surface expression and uncovered hundreds of candidate genes that may contribute to Siglec-F expression. Our results not only identified Siglec-F as the second-ranked candidate, but we identified other genes that likely modulate Siglec-F expression or trafficking. These genes include transcription factors such as Fos and NFKB2 and surface receptors such as M6PR. Our screen candidates are likely to include both direct regulators of Siglec-F expression and indirect regulators that maintain the AM-like state. In support of this prediction, we identified USP9x, a known regulator of TGF-β signaling, as a strong positive regulator of Siglec-F expression (52). In addition, we identified the enrichment of functional pathways previously associated with AM function, including the peroxisome, lipid metabolism, oxidative phosphorylation, and CYP. Given the previous links among PPAR transcription factors, peroxisome biogenesis, and lipid metabolism, our data strongly suggest that FLAMs recapitulate the metabolic makeup of AMs, which is central to their gene regulation (12, 54, 63). In further relationship to the metabolic state of FLAMs, we identified several CYP family members among our top candidates that regulate vitamin A and all-trans retinoic acid, known modulators of AM function (64, 65). Based on these findings, we posit that PPARγ expression drives lipid metabolism to induce the AM-specific transcriptional profile, resulting in Siglec-F expression. Future studies will be centered on testing this model and deeply validating the genetic screen to uncover novel regulatory mechanisms in FLAMs.

FLAMs are a promising model to study AM biology, yet some limitations remain. Although FLAMs maintain many AM-like phenotypes long-term, we observed variable expression of the AM marker CD11c over time. This suggests that there are other signals in addition to GM-CSF and TGF-β that are needed to fully recapitulate AM functionality ex vivo. The alveolar space is a highly complex microenvironment, with constant crosstalk between AMs and other cells (56). For example, our data show that FLAMs express TGF-β, yet this is not sufficient to maintain AM-like functions and continued Tgfbr1 and Tgfbr2 expression. Given that TGF-β is known to amplify Tgfbr1 and Tgfbr2, this suggests that the TGF-β produced by FLAMs is not biologically active. In vivo, latent TGF-β released by AMs is activated by αVβ6 integrins expressed on type II alveolar epithelial cells, resulting in increased levels of the active protein that can signal in an autocrine manner to maintain the unique phenotype of AMs (66). This feed-forward loop is absent ex vivo, which may explain why addition of exogenous active TGF-β prevents the loss of the AM-like phenotype in FLAMs. Other signals provided by type II alveolar epithelial cells and others such as lung-resident basophils likely regulate AM maintenance, but these signals are not modeled in our system (67). In the future the potential to combine the genetic tractability of FLAMs with in vivo transfer models may enable detailed dissection of this cross-regulation systematically. Intranasal transfer of TGF-β–cultured AMs was recently shown to repopulate the alveolar space. It will be important to test whether FLAMs could be similarly instilled into lungs lacking endogenous AMs, enabling rapid in vivo studies to better understand AM maintenance within the lung environment (27).

In summary, we developed FLAMs, a stable ex vivo model that can be used to study lung development, immunology, and toxicology. FLAMs are likely to shed new light on processes unique to AMs, such as phagocytosis, efferocytosis, and the removal of inhaled particles, by employing targeted or genome-wide genetic approaches. Taken together, the optimization and application of FLAMs provides an exciting, innovative model to thoroughly investigate AM biology.

Disclosures

The authors have no financial conflicts of interest.

Acknowledgments

We acknowledge Dr. Jack Harkema, Dr. Melissa Bates, Dr. Mikhail Givralin, Dr. Alexander Misharin, and the members of the Olive and Pestka laboratory for helpful discussions and input. We thank the MSU flow cytometry core for help with instrumentation and analysis, and Carol Flegler of the MSU Center of Advanced Microscopy for assistance in scanning electron microscopy.

Footnotes

  • This work was supported by startup funding to A.J.O. provided by Michigan State University, the Rackham Endowment Award (to A.J.O.), the Dr. Robert and Carol Deibel Family Endowment (to J.J.P.), as well as by National Institutes of Health Grants AI148961 (to A.J.O.), F31ES030593 (to K.A.W.), ES027353 (to J.J.P.), and T32ES030593 (to K.A.W.), U.S. Department of Defense Grant W81XWH2010147 (to A.J.O.), and the U.S. Department of Agriculture (National Institute of Food and Agriculture HATCH Grant 1019371 [to A.J.O.]).

  • The online version of this article contains supplemental material.

  • Abbreviations used in this article

    AM
    alveolar macrophage
    BMDM
    bone marrow–derived macrophage
    cSiO2
    crystalline silica
    CYP
    cytochrome P450
    FLAM
    fetal liver–derived alveolar-like macrophage
    GSEA
    gene set enrichment analysis
    iBMDM
    immortalized BMDM
    KEGG
    Kyoto Encyclopedia of Genes and Genomes
    MAGeCK
    model-based analysis of genome-wide CRISPR-Cas9 knockout
    MFI
    mean fluorescence intensity
    RRA
    robust rank aggregation
    α-RRA
    modified RRA
    sgRNA
    single-guide RNA
    TRM
    tissue-resident macrophage

  • Received January 28, 2022.
  • Accepted January 28, 2022.

This article is distributed under the terms of the CC BY-NC 4.0 Unported license.

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