The placenta is a heterogeneous organ whose development involves complex interactions of trophoblasts with decidual, vascular, and immune cells at the fetal–maternal interface. It maintains a critical balance between maternal and fetal homeostasis. Placental dysfunction can lead to adverse pregnancy outcomes including intra-uterine growth restriction, pre-eclampsia, or pre-term birth. Exposure to environmental pollutants contributes to the development of placental abnormalities, with poorly understood molecular underpinning. Here we used a mouse (C57BL/6) model of environmental pollutant exposure by administration of a particulate matter (SRM1649b at 300 μg/day/mouse) suspension intra-nasally beginning 2 months before conception and during gestation, in comparison to saline-exposed controls. Placental transcriptomes, at day 19 of gestation, were determined using bulk RNA-seq from whole placentas of exposed (n = 4) and control (n = 4) animals and scRNAseq of three distinct placental layers, followed by flow cytometry analysis of the placental immune cell landscape. Our results indicate a reduction in vascular placental cells, especially cells responsible for structural integrity, and increase in trophoblast proliferation in animals exposed to particulate matter. Pollution-induced inflammation was also evident, especially in the decidual layer. These data indicate that environmental exposure to air pollutants triggers changes in the placental cellular composition, mediating adverse pregnancy outcomes. In addition, the placenta produces a plethora of hormones necessary for the physiological adaptation necessary during pregnancy3. The placenta also serves as an effective barrier protecting the fetus from toxins and pathogens4. Since the placenta co-exists with the fetus, it also encounters various intra-uterine exposures, and experiences certain detrimental effects, which in turn could negatively impact maintenance of a pregnancy or fetal health2. Such imposition of placental changes ultimately adversely affects the well-being of mother and conceptus2. Environmental exposures from air pollution, especially particulate matter originating in urban areas can have far-reaching effects on maternal well-being and fetal development7, and have been implicated in adverse pregnancy outcomes, such as preterm labor8, gestational diabetes mellitus9 or pre-eclampsia10 presenting with fetal growth restriction and low birth weights12. In order to understand the effects of environmental exposures on mother and conceptus, it is important to study the placenta. In particular one needs to understand the complex cellular and signaling patterns underlying placental development.The impact of the intra-uterine environment upon the conceptus can be far reaching, given that changes incurred during this vulnerable and critical developmental period have long standing implications with changes of permanency. Much of this adaptive imprint is delivered to developing tissues by changes in gene expression and cellular content. The placenta, an organ that co-habitats with the conceptus, acts as a functional interface delivering oxygen and nutrients to the developing fetus1. A comprehensive assessment of cellular heterogeneity is traditionally performed by immunophenotyping which can be biased and relies on a small set of pre-selected markers, limiting the cell types that can be inspected13. Alternatively, Bulk RNA sequencing (RNA-seq) offers an unbiased approach to tissue profiling with greater resolution, accounting for the dynamic nature of the transcriptome14. RNA-seq can also help to identify novel transcripts, alternately spliced genes, and allele-specific expression. However, RNA-seq typically represents an average of gene expression across millions of cells which may obscure cellular heterogeneity, especially in organs or tissues with multiple cell types15. To overcome this barrier, one could isolate single cells and capture their transcripts by employing single cell RNA-seq, a technique that can assess the cell population structure in depth and the nuances of various cell signaling pathways with unprecedented resolution16. Single cell transcriptomics performed by 10X genomics is an established technology that is helpful in deciphering cell-specific gene expression in complex organs and tissues subjected to various environmental stressors17. The introduction of this technology has proven to be highly useful in unraveling the major and minor cell types present in a sample, in addition to identifying differentially expressed genes. This has resulted in the discovery of various novel cell types and their abundance under normal and pathological circumstances19.Tissues, including the placenta, are comprised of diverse cell types with distinguishable developmental or functional origin that form a complex niche21 and in the presence of pre-eclampsia20. Similarly, murine placental scRNA-sequencing has been performed under normal circumstances24. However, given distinct differences between human and murine placentas25 and the fact that manipulations during human pregnancy are not possible, the development of genetic and pathological murine pregnancy models has been applied to various conditions. Examples include intrauterine growth restriction26 due to maternal calorie restriction27, maternal diabetes28 and hypertension27, among others. More recently, the effect on pregnancy of exposures to air pollutants has gained increasing visibility resulting in the development of multiple mouse models30. Using one such nasal instillation gestational model, we sought to assess the impact of air pollutants on the late gestation placental transcriptomics and cellular composition. We hypothesized that employing single-cell transcriptomics and deconvolution of placental bulk transcriptomics will provide the basis for identification of cellular composition, setting the stage for application to various murine models beyond the one we have tested here. To test this hypothesis, we studied the late gestational placentas divided into three layers, namely the decidual, junctional and labyrinthine layers and identified the cellular and gene expression signatures.Previous studies have examined the human placenta during the first trimester and provided information regarding the cellular content and transcriptomics under normal circumstancesSix animals (3 exposed to air pollution and 3 controls) were used for scRNAseq analysis. Placentas were collected from each pregnancy, separated into 3 placental layers and tissue from the same placental layers were pooled for each group, yielding a total of six samples (two treatments and three layers). A total of 40,739 cells were processed, of which 9007 cells were extracted from the control (CON) decidual, 8654 from the air pollution (AP) decidual, 4410 cells from CON junctional, 5665 from AP junctional, 5978 cells from CON labyrinthine and 7025 from the AP labyrinthine layers Table .31. The majority of these clusters were grouped together based on cell-type similarity, and all cell types could be found in both AP and CON conditions  distribution is depicted in Fig. ons Fig. , certain32 to perform the deconvolution analysis with the bulk RNAseq data from AP and Control samples as inputs , as GEDIT was shown in a recent report to compare favorably with other deconvolution tools33. A second tool, designed specifically for scRNAseq references, MuSic34 was also used to verify the deconvolution results. While both tools performed similarly in identifying cell types present in bulk  as calculated for the CON group was 0.9 and for the AP group was 0.95  cells including Allograft Rejection, Inflammatory Response, E2F Targets, Myc-Targets-V1 in the AP group versus CON  in bulk RNAseq out of which 48 genes were significantly upregulated and 70 genes were downregulated in placentas of mice exposed to AP Fig. . We used36. However, these associative studies lack information regarding the cellular mechanisms responsible for such outcomes. Exposure to a low level of PM (namely PM2.5) is known to damage trophoblast cells either by direct cellular endocytosis of PM or due to a local oxidative stress or inflammatory response to the particle and other components such as endotoxins or poly aryl hydrocarbons (PAHs), within the pollutants37. The placenta is essential for a successful pregnancy and for maintaining the health of both mother and fetus. The mature placenta is heterogeneous in nature with a panoply of diverse cell types expressing unique transcriptomes39. However, conventional bulk RNAseq can provide an average expression of signals for an ensemble of cells, without distinguishing between the cell types. We therefore aimed to resolve this issue by adopting scRNAseq using single cell suspensions obtained from three different compartments of the placenta. While scRNAseq is a useful tool for identifying cell-type specific profiles and detecting markers, this technique is usually computationally challenging with a low signal to noise ratio due to the high variability. This method is also not ideal for characterizing the cell type proportion from a solid tissue as the cell dissociation process by itself can introduce bias as larger cells such as the syncytiotrophoblasts may be excluded by the 10X genomic platform40. By contrast, bulk RNA-seq measures the average expression of genes from all cell types, an outcome of cell type specific gene expression weighted more by the cell type proportion, and provides a better sequencing depth, thereby increasing the resolution of gene expression analysis. Hence, bulk RNA-seq is more cost-effective when evaluating adequate numbers of biological replicates in order to attain sufficient experimental power. However, unlike scRNAseq, bulk RNA-seq lacks cell- or region-specificity, which could be important to the clinical condition as certain cell types may be more involved than others in a specific response. In our study, scRNAseq data obtained from placenta of AP exposed mice showed damaged trophoblast and placental vasculature along with inflammation. These findings were further validated by estimating the abundance of each placental cell type from bulk transcriptional profiles of heterogeneous samples using signature genes derived from the scRNAseq analysis. Since, neither of these methods are ideal, each having their own limitations, and the fact that certain placental cells, namely trophoblasts, are highly heterogeneous, we used a combined approach utilizing signature genes and cell type abundance inferred by deconvolution of bulk RNAseq. In addition, we used flow cytometry to assess the abundance of immune cells arising from three different placental compartments. We noticed some discrepancies in the cell abundance of macrophages and granulocytes detected by scRNAseq and estimated by bulk RNAseq/Flow cytometry. There can be multiple sources of these discrepancies, such as technical bias or lack of biological replicates in the scRNAseq or inadequate cell-proportion estimation from bulk RNAseq. These limitations highlight the importance of employing multiple methodologies to verify cell abundances when estimating tissue heterogeneity.A strong association between exposure to particulate matter (PM) pollutants found in traffic related air pollutants, and pregnancy complications such as preterm labor or pre-eclampsia has been reported previously, indicating a pivotal role for placental dysfunction leading to these conditions43. Although these leukocytes are present throughout pregnancy, their abundance changes temporally with a reduction at term. Most of the immune cells in the decidual layer of the placenta are NK cells that regulate vascularization, thereby establishing utero-placental vascular connection and ensuring adequate blood flow from mother to fetus45. The presence of inflammation during pregnancy abnormally activates NK cells with perforin secretion, which is one of the main mediators of cytotoxicity. Mounting evidence has also linked adverse NK cell activation to reproductive failure in human and mouse52. In our study, we detected an increased abundance of NK cells in AP exposed placentas which can in turn adversely affect the placental cells that contribute towards maintaining its architecture and thereby normal structure.Multiple immune cell types including different leukocyte subtypes are recruited into different compartments of the placenta in response to chemokine gradients created by trophoblasts or stromal cells53. Accumulation of macrophages, plasma cells and T lymphocytes in the placenta have been reported in response to certain inflammatory conditions, such as chronic decidualitis, villitis of unknown etiology (VUE) and at times even in chronic chorioamnionitis54. Of note, T cell accumulation is also observed in solid organ transplant rejection55. VUE, is an inflammatory state described to mimic graft rejection, and noted in maternal auto-immune disorders58, which often complicates a pregnancy and at times leads to preterm labor. Normal pregnancies are characterized by immunological tolerance of the allogenic fetus, which is mediated by the absence or scarce presence of classical MHC class I receptors on placental trophoblasts designed to prevent NK or T cell mediated cytotoxic targeting within the decidua60. In the case of AP exposure, in our present study, we have seen increased numbers of cytotoxic CD8+ T cells not only in the decidua but also in the junctional and labyrinthine regions of the placenta.We have also observed increased numbers of M1 macrophages in the decidua, and CD8 T-cells in response to AP exposure. M1 macrophages act as antigen presenting cells and skew the T cell response towards TH1 mediated cellular immune response53. In parallel, increased abundance of progenitor trophoblasts and various subtypes of invasive spongiotrophoblasts were detected in response to AP exposure. These findings support a compensatory M2 response geared towards reducing the encountered AP induced inflammation whereas actively dividing trophoblasts may target the rebuilding of placental vasculature61 or the overall placental architecture. Alternately, the AP induced increase in progenitors may signify a downstream arrest in cellular maturation leading to premature yet terminal differentiation of certain trophoblastic cell types. It is intriguing to apply a similar analysis by deploying insights gained from scRNAseq towards deconvoluting bulk RNA-seq analyses from other conditions known to adversely affect placental vasculature and structural components, as encountered in our previous studies related to maternal caloric restriction induced reduction in uteroplacental blood flow26, or in murine models of pre-eclampsia62, and in genetically modified mouse models culminating in fetal growth restriction63. In addition to the advantage of applying such deconvolution strategies to other murine conditions of clinical significance in the human, the addition of spatial transcriptomics in the future may help decipher the spatial configuration of various identified cell types within their normal habitat within the placenta64, shedding additional light on the clinical problem at hand.GSEA analysis showed positive enrichment of the allograft rejection pathway and immune response pathway genes (predominantly expressed by macrophages or lymphocytes) in placentas exposed to AP. Besides M1 macrophages, there is also an increased abundance of M2 macrophages in the decidual region of AP placentas. Unlike M1, M2 macrophages are immunosuppressive and facilitate tissue remodeling67. In addition, while we did not decipher any changes in litter size or placental weights at gestational day 19, similar to previous reports66. We preliminarily observed cellular necrosis and hemorrhages histologically in placentas exposed to AP versus CON. In addition, we noted that a single dimensional immunohistochemical analysis of immune cells within placental tissue sections using antibodies against specific markers proved insensitive in detecting differences between the two groups, necessitating scRNAseq and flow cytometric analyses as described above.We did not examine the timing of birth or other birth outcomes as has previously been reported in rodents by others, where resorption of embryos, preterm birth and reduction in fetal weights emerged, in response to pre-gestational and gestational air pollution exposureAlthough the current study is limited due to pooling of biological replicates into a single sample to meet the requisite cell numbers necessary for scRNAseq, combining scRNAseq and bulk RNAseq along with Flow cytometry provides a good toolbox to characterize the effects of AP exposure on the placenta at the cellular and molecular levels. The cellular changes we have observed could provide the missing link between adverse pregnancy outcomes such as initiation of preterm labor or pre-eclampsia and AP exposure, thereby helping focus the development of preventive strategies for at-risk pregnancies.2.5 and ~ PM10, thereby mimicking traffic-related air pollutants. C57BL/6J mice obtained from Jackson Laboratories were housed in filter-topped cages under dark:light 12:12(h) cycling conditions with ad lib access to water and chow diet  within the University of California Los Angeles Animal Care Facility. Female mice were administered SRM1649b intranasally under light restraint, beginning from 2 months prior to gestation until 18th day of gestation  every third day or daily during gestation only (G1–G18). The experimental group received 20 μL (10 μL/nares) of SRM1649b (300 μg/day/mouse) suspension while the control group received 20 μL of sterile saline68. Placentas were collected at gestational d19, following laparotomy and hysterectomy, under 4% for induction and 1.25 to 1.5% for maintenance of the inhalational isoflurane anesthesia. Protocols for the care and use of mice were approved by the Animal Research Committee of the University of California following the guidelines provided by the National Institutes of Health. The study was carried out in compliance with the ARRIVE guidelines69.The Airborne PM of Standard Reference Material (SRM1649b) was purchased from the National Institute of Standards and Technology . Prior to exposure, SRM1649b was re-suspended in sterile saline and sonicated for 15 min to obtain a final concentration of 15 μg/µL, and ensuring uniform suspension of dissoluble PAHs, PCBs, and inorganic constituents with a heterogeneous PM size reduced to a range between ~ PMRNA was extracted with Direct Zol RNA MiniPrep kit from Zymo  using manufacturer’s instructions. RNA sequencing libraries were prepared using KAPA Stranded mRNA sequencing kit  following manufacturer’s protocol. Briefly, 100 ng of total RNA from each sample was used as the starting material with biological quadruplicates from each group. mRNA was captured using magnetic oligo-dT, fragmented by heat and Mg, and reverse transcribed to cDNA using random primers. After 2nd strand synthesis, cDNA was end-repaired, index adapter-ligated and PCR amplified. SPRIselect beads  were used to purify nucleic acids after each step of the library preparation. RNA sequencing libraries were sequenced by the HiSeq-4000 sequencer .70. Single end reads from individual libraries were aligned to the mouse reference genome (mm10) using STAR-2.6 aligner71. From each library, uniquely aligned reads were used to obtain raw counts for all the Mus musculus genes  with the featureCounts tools from the Subread package72. Further analysis and data visualization were performed using various R packages. DESEq273 was used to calculate the size factors and Raw read counts were scaled accordingly to normalize for library size. Low abundant genes were filtered out at this point. Differential expression was calculated between control and AP samples using a negative binomial generalized linear model, and correction for multiple testing was represented as FDRs  using the Benjamini–Hochberg method. FDR values < 0.05 were considered statistically significant.Raw sequences were demultiplexed using a custom script. The demultiplexed fastq files are available at GEO (GSE178233). Quality of the raw fastq files was reviewed using FastQChttps://www.flowjo.com/.Placentas collected at G19 were extensively washed with cold PBS twice and the three different layers  were separated. The whitish gray decidual layer was gently peeled off from the rest of the placenta while maintaining tissue integrity. Junctional zone separation from the labyrinth was done microscopically with a sterile blade. Decidual, junctional and labyrinthine layers from multiple placentas were pooled to obtain adequately optimal numbers of cells for analysis. Separated layers were minced into small pieces and digested with an enzyme cocktail [collagenase (1 mg/ml)  and DNase (20 µg/ml) ] in RPMI 1640 ] for 60 min on a shaker. Single cell suspensions were prepared by passing through 100 μm cell strainers  and numbers of live cells were determined by trypan blue  after lysing erythrocytes with ACK Lysis buffer  for 3 min at room temperature. These freshly isolated single cell suspensions were directly subjected to scRNAseq using the Chromium Single Cell 3′-Library & Gel Bead Kit . For flow cytometry, cells were incubated with pre-optimized concentrations of fluorescence tagged antibodies marking different cell lineages. A list of antibodies used for flow cytometry has been incorporated in the Supplementary Table Library construction: Single cell RNAseq (scRNAseq) libraries were prepared using the Chromium Single Cell 3′-Library & Gel Bead Kit  following manufacturer’s instructions. Briefly, placental single cell suspension was prepared as mentioned above and loaded onto a Chromium Single Cell Controller to generate single cell gel beads in emulsion (GEM). GEM-Reverse transcription (RT) was performed, and single stranded DNA was cleaned with Dyna beads. cDNA was then fragmented, end-repaired, and A-tailed. Furthermore, adaptor ligation was performed using the Chromium Single Cell 3′-kit followed by post-ligation cleanup using the SPRIselect Reagent.75 was used for quality control, analysis, and exploration of the data using a standard pipeline. Cell clusters were annotated using the Mouse Cell Atlas v.2.0 marker genes as a guide31.The raw fastq files were processed with Cell Ranger v4.0 (10X Genomics) using the cellranger count and cellranger aggr pipelines against the mm10 reference package provided. Seurat v3.032 using a reference matrix from averaged “pseudo-bulk” single-cell RNA-seq data, using the default parameters. Further verification of the results obtained by GEDIT was conducted using MuSiC21. For z-score calculation, log normalized read counts obtained from DESEq2 analysis and Z-scores were calculated by subtracting the average read counts (for a single gene) from the read counts of each sample and then dividing the result by the SD of all the read counts following the formula:1: 1st sample; Gn: nth sample where n is the total number of samples.Deconvolution of bulk RNA-seq was executed using GEDIT v1.6scRNA data were obtained from previous reports76.To obtain aggregated Z-scores for each cell type, an average of z-scores from the top 10 signature genes for individual scRNA cell-types were calculated for each sample. Signature genes for cell types missing in our 77 with various packages like Seurat75, ggplot278, clusterprofiler79, ComplexHeatmap80 and final figures were combined using Adobe Photoshop CS6 (2012).Unpaired T tests were performed to compare differences in means between CON and AP groups. Any exceptions have been indicated in the text. Data visualization was performed using RSupplementary Information.


We also demonstrate, for the first time to our knowledge, that under normal conditions PN mitochondrial Ca2+ uptake shapes nucleoplasmic Ca2+ transients (CaTs) and limits nucleoplasmic Ca2+ loading. The loss of PN mitochondrial Ca2+ buffering capacity translates into increased nucleoplasmic CaTs and may explain disproportionate rise in nucleoplasmic [Ca2+] in failing cardiomyocytes at increased stimulation frequencies. Therefore, a previously unidentified benefit of restoring the mitochondrial Ca2+ uptake may be normalization of nuclear Ca2+ signalling and alleviation of altered excitation–transcription, which could be an important therapeutic approach to prevent adverse cardiac remodelling.Mitochondrial dysfunction in cardiomyocytes is a hallmark of heart failure development. Although initial studies recognized the importance of different mitochondrial subpopulations, there is a striking lack of direct comparison of intrafibrillar (IF) versus perinuclear (PN) mitochondria during the development of HF. Here, we use multiple approaches to examine the morphology and functional properties of IF versus PN mitochondria in pressure overload-induced cardiac remodelling in mice, and in non-failing and failing human cardiomyocytes. We demonstrate that PN mitochondria from failing cardiomyocytes are more susceptible to depolarization of mitochondrial membrane potential, reactive oxygen species generation and impairment in CaThis article is part of the theme issue ‘The cardiomyocyte: new revelations on the interplay between architecture and function in growth, health, and disease’. The coordinated regulation of Ca2+ cycling under the conditions of different workload demand is achieved by the close physical proximity of the main Ca2+ cellular store, the sarcoplasmic reticulum (SR), and mitochondria distributed throughout the cell. At the junction between the SR and mitochondria, a space known as the subcellular microdomain, local Ca2+ fluxes of high magnitude lead to mitochondrial Ca2+ uptake , which enhances tricarboxylic acid cycle dehydrogenases, leads to faster NAD(P)H reduction and finally increases ATP generation by feeding the electron transport in the respiratory chain .2+ uptake is compromised in HF  in failing cardiomyocytes at increased stimulation frequencies. Therefore, normalization of mitochondrial Ca2+ regulation may be a novel therapeutic approach to restore altered Ca2+-mediated transcription and prevent adverse cardiac remodelling.Accumulating knowledge on mitochondrial dysfunction in HF has been recently reviewed  and it c. 2https://doi.org/10.5061/dryad.pvmcvdnn9. Materials and methods are described in detail in the electronic supplementary material.The data supporting findings of this study are available from the Dryad Digital Repository: All procedures involving animals were carried out in accordance with the Federal Act on the Protection of Animals  or the NIH Guide for the Care and Use of Laboratory Animals (UC Davis) and were approved by the Institutional Animal Care and Use Committee. Human hearts (from patients and organ donors whose hearts could not be used for transplantation) were acquired via collaboration with the Division of Cardiac Surgery . The use of human samples was approved by the Ethical Committee of the Medical University of Graz, and all experimental procedures were carried out in accordance with the Declaration of Helsinki.. 3 (a)a), and in the space between the two nuclei in typically binucleated ventricular cells (figure 1c). Quantification of mitochondrial size and shape by individual tracing of mitochondria defined as organelles enclosed by a double contoured membrane in cytoplasmic and PN spaces showed that mitochondrial perimeter and aspect ratio are significantly different in the two subpopulations (figure 1b), with PN mitochondria being smaller and more spherical. Interestingly, IF mitochondria in TAC-operated mice at seven weeks post-intervention appeared smaller and rounder, and were increased in number per area when compared with controls (figure 1d), while these parameters remained unchanged for PN mitochondria. These data suggest a shift of typical IF mitochondria towards a phenotype more resembling that of PN mitochondria during the development of HF. As PN mitochondria are critically involved in mitochondrial genesis and turnover, this phenotypic shift could be a result of premature mitochondrial recruitment and/or delayed degradation with important functional consequences for cardiomyocyte bioenergetics.To address potential changes in the organization and function of mitochondrial subpopulations during pressure overload-induced HF, we first performed confocal and electron microscopy (EM) imaging of intact adult ventricular cardiomyocytes and mouse myocardium preparations, respectively. As previously observed in EM images of rabbit hearts , ultrastfigure 1 (b)a,b). However, myocyte growth was even greater, resulting in a reduced mitochondrial volume as a fraction of cell volume (figure 2c). This reduced relative mitochondrial volume was already decreased substantially for IF mitochondria at one week post-TAC, with similar values obtained at seven weeks post-TAC. These values are in remarkable agreement with a previous study using the same model, in which an advanced three-dimensional stereology method for quantification of cardiomyocyte composition was used  to assess mitochondrial Ca2+ uptake in IF and PN mitochondria from control and failing mice (c–e). In agreement with previous work  studied. TAC caused a dramatic reduction in mitochondrial Ca2+ uptake in both IF and PN mitochondria at each of the three values of [Ca2+] studied . Again, functional capability to sequestrate Ca2+ was disproportionally impaired in PN mitochondria, which also exhibited a higher threshold level of [Ca2+] for uptake compared with IF mitochondria . The seemingly faster Ca2+ uptake observed in TAC versus sham PN mitochondria at higher [Ca2+] (2 and 10 µM) is likely due to already saturated mitochondrial [Ca2+] after exposure to 1.35 µM Ca2+ in sham mice.Compromised mitochondrial Caing mice c–e. In aous work , we obsechondria c,e; howe studied c,d. Agai2+ uptake by mitochondria plays an important role in (sub)cellular Ca2+ cycling, and its dysregulation has a profound effect on excitation–contraction coupling and cytoplasmic [Ca2+] in HF. As PN mitochondria are particularly functionally impaired in TAC animals and failing human hearts, this raises an interesting question as to whether altered Ca2+ uptake by PN mitochondria may have a measurable effect on nucleoplasmic Ca2+ homeostasis in HF.Ca (e)2+ uptake on nucleoplasmic CaTs in ventricular myocytes isolated from healthy mouse hearts (a–c). Cells were simultaneously labelled with the Ca2+ indicator Fluo-4 for quantification of subcellular CaTs and TMRM for detection of mitochondrial localization in the absence and presence of the specific MCU inhibitor Ru360 (10 µM) [2+ uptake to the extent that mitochondrial Ca2+ uptake on a time scale of action potential-induced CaTs is essentially blocked [2+] and high TMRM signal surrounding the nucleus on both poles , compatible with the idea that PN mitochondria may buffer Ca2+ around the nucleus during electrically stimulated CaTs, therefore shaping the nucleoplasmic-to-cytoplasmic [Ca2+] gradients in cardiomyocytes during the cardiac cycle. Similar regions were observed in cardiomyocytes from non-failing human hearts (d), hence strengthening their general applicability and clinical relevance.To address this question, we first tested the effect of pharmacological inhibition of mitochondrial Cae hearts a–c. Cell (10 µM) ,30. Ru36 blocked . In the th poles a,b, compn hearts d, hence 2+ levels and amplitude . Notably, increased Ca2+ cycling in PN regions also translated into higher nucleoplasmic CaTs, in terms of both diastolic [Ca2+] and amplitude of CaTs. These data raise the possibility that the lack of PN mitochondrial Ca2+ uptake as observed in TAC cardiomyocytes may lead to more rapid propagation of CaTs from the cytoplasm to the nucleus, and moreover, to the disproportional increase in nucleoplasmic versus cytoplasmic CaTs in diseased myocytes, especially at higher pacing rates where mitochondria are expected to take up more Ca2+ [2+] in the nucleoplasm to a much larger extent than in the cytoplasm, leading to the activation of nuclear CaMKII and consequential nuclear export of the transcriptional regulator HDAC4  in failing cardiomyocytes, especially in the nuclear compartment. Notably, Ru360 had no effect on cytoplasmic [Ca2+] in either sham- or TAC-operated mice. The reduction in nuclear CaT amplitude seen in TAC  is explained by the dramatic rise in diastolic nuclear [Ca2+] in failing TAC myocytes , which limits nuclear Ca2+ efflux between beats that is caused by altered Ca2+-regulating proteins and nuclear envelope structure  by healthy PN mitochondria, which is revealed by Ru360 treatment, is necessary to accelerate nuclear [Ca2+] decline, lower diastolic nuclear [Ca2+] and lower nuclear CaT amplitudes to limit Ca2+-dependent transcriptional signalling. The loss of this Ca2+-buffering ability in failing heart PN mitochondria is why Ru360 no longer makes things worse .We conclude that the normal buffering of PN [Ca. 42+ uptake across a range of different [Ca2+], reduced mitochondrial membrane potential and increased ROS production in failing cardiomyocytes exposed to increased frequency of pacing. Depressed Ca2+ uptake in PN mitochondria has a direct effect on nucleoplasmic CaTs and may play a causal role in disturbed excitation–transcription coupling and progression of maladaptive cardiac remodelling. These data identify a novel subcellular mechanism of functional decline during HF development that could help the advancement of new cardioprotective strategies and/or better understanding of molecular mechanisms underlying the beneficial effects of current cardiometabolic therapies.The results of the present study indicate that PN mitochondria are functionally altered to a higher degree than IF mitochondria in failing cardiomyocytes, as manifested by the severely depressed Ca (a)et al. [2+] rise in PN mitochondria Ψm, Ca2+ uptake and ATP production [2 autofluorescence under conditions of increased cell work induced by high-frequency pacing. Quantitative analysis in TAC cardiomyocytes revealed nearly maximal FAD/FADH2 ratio (oxidized) at basal conditions, and the TAC cardiomyocytes were significantly more oxidized than sham cardiomyocytes. In addition, it has recently been demonstrated that pressure overload induced by TAC resulted in a significant increase in the expression of Nox2 oxidase subunits and a more oxidized state overall as assessed by the GSH/GSSG ratio [+, Ca2+ and the mitochondrial redox environment, a concept comprehensively elaborated upon by Cortassa et al. [2 ratio with minimal ROS generation in sham cardiomyocytes shifted to depressed Ca2+ uptake, and severely oxidized FAD/FADH2, with a concordant increase in ROS generation in TAC mice.IF mitochondria retained beyond their regular turnover rate would be expected to show slightly decreased functional properties and be especially vulnerable to stress in hypertrophic and failing cardiomyocytes. In addition, those sent to be recycled in PN spaces would show more pronounced signs of functional exhaustion, with potential consequences to overall cardiomyocyte vulnerability including functional and structural outcomes. Pressure overload-induced HF has been shown to result in increased mitochondrial oxidative stress and ROS generation and reduced Δoduction ,39, obseoduction  demonstrSG ratio . It is aa et al. . This coThe most novel finding here is that, while IF mitochondria showed signs of exhaustion mostly under stress, PN mitochondria from failing myocytes showed signs of functional deterioration already at baseline and high frequency of pacing pushed them into even more oxidative stress and functional decline figures  and 4. T2+, especially at higher cytoplasmic Ca2+ levels. In HF, diminished SR Ca2+ release and increased intracellular Na+ levels depress mitochondrial Ca2+ uptake, which impairs their capacity for sustaining optimal matrix NAD(P)H redox potential. This in turn boosts oxidative stress, especially upon increased workload. Here, we found severely diminished IF mitochondrial Ca2+ uptake in failing cardiomyocytes, and notably, even more depressed Ca2+ uptake by PN mitochondria  protected mice from developing a full-blown HF phenotype [2+ uptake had clear whole-cell functional outcomes in isolated rabbit atrial myocytes [2+ uptake with the polyamine compound spermine.Another important parameter of mitochondrial fitness with implications in a variety of cellular functions is their ability to take up Cachondria . Althougerfusion , recent  [et al.  showed thenotype . In addimyocytes . Specifi (c)2+ uptake in preventing cardiac remodelling and its transition to HF could be their ability to specifically influence (peri)nuclear Ca2+. This idea is supported by the prominent mitochondrial accumulation in PN regions and the space between the two nuclei in characteristically binucleated ventricular cells , which can phosphorylate histone deacetylase 4 (HDAC4) and drive its nuclear export. This process mediates the de-repression of Mef2-dependent transcription, implicated in the development of HF [2+ levels may, on the other hand, ensure a pool of active PN CaMKII which will keep rephosphorylating any exported HDAC4 that becomes dephosphorylated after leaving the nucleus. This could prevent HDAC4 from reentering the nucleus and reinforce the transcriptional signalling of activated CaMKII.The increase in (peri)nuclear Cagulation . For exant of HF . Increas2+ crosstalk occur in the slower disease progression observed in animal models and patients who suffer from HF with preserved ejection fraction.One important aspect to consider when interpreting the present data is the variety of HF phenotypes in regard to left ventricular ejection fraction, pathophysiology, underlying triggers , mechanisms of progression and response to treatment. The development of powerful techniques for comparative transcriptome analysis of cardiomyocytes and non-myocytes on a single cell level identified numerous aetiology-specific alterations in gene expression, pointing to different underlying mechanisms of the disease progression . This hi2+ levels—contribute to the development and progression of hypertrophy and HF. Normalization of mitochondrial Ca2+ regulation may therefore be a novel therapeutic approach to restore altered Ca2+-mediated transcription and prevent adverse cardiac remodelling.Taken together, the present study implicates fitness and functionality of PN mitochondria as an important determinant of cardiac remodelling that may—via shaping nucleoplasmic Ca


Pogona vitticeps), two geckos (Paroedura picta and Coleonyx variegatus) and the painted turtle (Chrysemys picta). We first performed a histological characterization of the spermatogenesis process in both the bearded dragon and the painted turtle. We then analyzed prophase I dynamics, including chromosome pairing, synapsis and the formation of double strand breaks (DSBs). We show that meiosis progression is highly conserved in reptiles with telomeres clustering forming the bouquet, which we propose promotes homologous pairing and synapsis, along with facilitating the early pairing of micro-chromosomes during prophase I . Moreover, we detected low levels of meiotic DSB formation in all taxa. Our results provide new insights into reptile meiosis.During meiotic prophase I, tightly regulated processes take place, from pairing and synapsis of homologous chromosomes to recombination, which are essential for the generation of genetically variable haploid gametes. These processes have canonical meiotic features conserved across different phylogenetic groups. However, the dynamics of meiotic prophase I in non-mammalian vertebrates are poorly known. Here, we compare four species from Sauropsida to understand the regulation of meiotic prophase I in reptiles: the Australian central bearded dragon ( This follows a tightly regulated progression of chromosome condensation and folding, coupled with changes to the epigenome and gene expression  pression . Meiosisbouquet  based on the dynamics of meiotic chromosomes and their telomeres  and the western-banded gecko (Coleonyx variegatus) as representatives of Gekkota (geckos), the Australian central bearded dragon (Pogona vitticeps) as a representative of Iguania , and the painted turtle (Chrysemys picta) as a representative of Testudines (turtles). These species are emerging models for thermal and reproductive physiology (C. picta), although XY and ZW systems are also present in different lineages  were obtained from captive colonies in Canberra  at the end of the breeding season (February). Male ocelot geckos  and male western banded geckos  were originated from breeding colonies in Charles University in Prague (Czech Republic). Male painted turtles  were wild-caught in Iowa (United States) at the end of the breeding season under appropriate permits from Iowa’s DNR.Male bearded dragons . Cultures were established by disaggregating tissue with a scalpel blade and resuspending cells in AmnioMAX. Cell cultures were incubated at 28°C in 5% COFor karyotyping, cells were arrested in metaphase by adding 80 μl of Colcemid (10 μg/ml) to 10 ml of medium for 2 h and then trypsinised. Cells were centrifuged down at 600 xg for 5 min and resuspended in 5 ml of hypotonic solution (0.075M KCl) for 30 min at 37°C. Chromosomes were then fixed by addition of fixative solution (3:1 methanol/acetic acid) and metaphase spreads were obtained by dropping 15 µl of cell suspension onto a cleaned dry slide. Slides were baked at 65°C for one hour and kept at −20°C until use. Metaphases were stained homogenously with DAPI for the karyotype analysis.Testes from the bearded dragon and the painted turtle were collected for histological procedures. Briefly, testes were fixed overnight in Bouin’s solution . Then, samples were dehydrated, cleared and embedded in paraffin using standard procedures. Sections (7 µm) were stained with PAS-hematoxylin.Testicular biopsies were obtained immediately after animal dissection and processed as previously described  in orderImmuno-staining of meiocytes was performed using the following primary antibodies: rabbit antibody against SYCP3 , rabbit antibody against SYCP1 , rabbit antibody against TRF2 , mouse antibody against RNA pol II , rabbit antibody against RAD51 , rabbit antibody against RPA32/RPA2 , mouse antibody against MLH1 , rabbit antibody against MLH1  and rabbit antibody against γH2AX .Fluorochrome-conjugated secondary antibodies were used for detection . Antibodies were diluted in PBST (Tween 0.05% in PBS). Primary antibodies were incubated overnight at 4°C in a humid chamber and secondary antibodies for 1 h at 37°C in a humid chamber. After washing away the excess of secondary antibodies, DNA was counterstained with anti-fade solution (Vectashield) containing 8 μg/ml DAPI .PAS-hematoxylin–stained tissue sections were analyzed on an Olympus CH2 microscope, and images were captured using a Zeiss Axiophot Microscope and Olympus C5060 camera. For fluorescent sample analysis and image capturing, a Zeiss Axioskop fluorescence microscope connected to a ProgRes Jenoptik camera was used. The image capture software ProgRes CapturePro was employed for image acquisition and image processing.bouquet was analyzed as the percentage of foci per cell located in the bouquet region, previously delimited as the area where synaptonemal complex  begins to assemble and SYCP3 intensity is higher. Only cells with a well-defined bouquet were included in the analysis.The accumulation of foci in the bouquet was determined using two-sided Mann-Whitney U-tests. The critical value for statistical significance was p < 0.05 for all tests. Each plot or its figure legend indicates the statistical methods and corresponding p-values. All boxplots are represented as centre lines (median), box limits  and whiskers (largest and lowest data points inside the first and third quartiles plus 1.5 times the interquartile range).Statistical significance for the DSB analysis as RPA and RAD51 foci, and for the analysis of the percentage of DSB foci in the P. vitticeps) and the painted turtle (C. picta) (We first characterized spermatogenesis progression in the bearded dragon (. picta) , followi. picta) . Both th. picta)  and the . picta)  had a hi. picta)  and othe. picta) .In both species, spermatogonia (A and B) were restricted to the basal lamina . Type A Spermiogenesis (the differentiation and maturation of sperm) encompasses a longer period than previous stages as large populations of round and elongating spermatids were observed . SpermioWe then analyzed the meiotic chromosome pairing strategies in all four species. Chromosome pairs in all four reptile species largely differ in size . The chrAxial elements of the synaptonemal complex labelled with anti-SYCP3 were used to classify spermatocytes into the different prophase I stages  was detected in spermatocytes of all four species, with increasing signal intensity through prophase I , mirroriWe then analyzed the dynamics of DSB formation by immunodetection of the recombination proteins RPA (Replication Protein A) and RAD51 (Radiation Sensitive 51)  as no anp ≥ 0.05, p < 0.001) and pachytene  (p < 0.001), intermediate values at late zygotene  and higher RPA foci at pachytene compared to the ocelot gecko   ; Table 1p ≥ 0.05), whereas the mean was higher in the bearded dragon compared to the ocelot gecko by early zygotene  and late zygotene (p < 0.001) (p < 0.001) except in pachytene   were detected for RAD51, which we detected in spermatocyte spreads of the painted turtle, the ocelot gecko and the bearded dragon ; Table 1 ≥ 0.05) ; Table 1bouquet region at early stages of prophase I  cystic, in which developing germ cell syncytia are individually encapsulated by Sertoli cells as observed in fish and amphibians, and (ii) noncystic, where spermatogenesis takes place in seminiferous tubules  . In specBoth the histological and cytological characterization presented here for reptiles revealed that spermatogenesis progression is noncystic and highly conserved with respect to cell morphology and distribution. Analysis of the histological distribution of different germ cell types within the seminiferous epithelia revealed that both the bearded dragon and the painted turtle showed similar patters to those described in eutherian mammals  and othePogona barbata classified testis as (i) regressed (only spermatogonia present), (ii) developing (spermatocytes or spermatids present), and (iii) spermiogenic (spermatogenesis and mature sperm present)  the regenerative phase, which occurs in the spring, and (ii) the degenerative phase, that begins in summer . So, thepresent) . Consistpresent)  and sincbouquet, presumably promoting homologous pairing and synapsis. The bouquet first appeared from leptotene to late zygotene, depending on the species. This feature is shared with zebrafish, suggesting that early telomere clustering is ancestral and has been retained over almost 400 million years of vertebrate evolution.We found that the progression of prophase I was highly conserved among the reptiles examined. In most species, chromosomes were organized into chromosome axes but were not yet synapsed at leptotene. This was coupled with the formation of DSBs . In our bouquet. This suggests that the bouquet facilitates SYCP3 loading and synapsis of homologous chromosomes (both macro and micro-chromosomes) from the telomeres towards the central regions of the chromosomes, at the same time as micro-chromosome synapsis. Complete homologous synapsis of micro-chromosomes was observed from early zygotene to pachytene. This was coupled with a polarization of DSBs towards telomeres, a notable difference compared to some mammal species where DSBs are distributed more homogenously across the genome as the SC is being assembled  occur genome-wide during leptotene in eutherian mammals . MarsupiThe first alternative agrees with previous cytological data. Specifically, studies in disparate taxa show that the total number and distribution of COs  on a specific chromosome depends on several factors, such as chromosome size, and an individual’s sex and age . Larger The second hypothesis derives from the observation that recombination rates may vary by the presence of different genetic determinants of DSBs induction, such as PRDM9 . PRDM9 iAs the use of non-model species can be challenging, future studies with a larger number of animals per species will be desirable to capture inter-individual variability in recombination rates. Additionally, here we report results obtained with two early markers of meiotic DSBs (RAD51 and RPA) in four different reptile species, but the use of direct measures of COs were precluded. Thus, future research focussed on MLH1 foci (a marker of COs) together with the analysis of chiasmata in metaphase I will provide a comprehensive view of the recombination dynamics in reptiles.Overall, our findings provide new insights into meiotic chromosome dynamics and double strand break formation during reptile spermatogenesis. Shared histological patterns observed between squamates and turtles suggest that they represent the ancestral state. However, future research across more species is warranted to asses conservation of this ancestral pattern across other sauropsids. Understanding the intricacies of the mechanisms regulating chromosome synapsis, recombination and segregation during meiosis progression across vertebrates will further determine the genomic basis of biodiversity, and how it may be affected by ecotoxicological and other environmental changes.


Few patients with suicide risk are counseled on lethal means safety by health providers. This study tested the feasibility of different delivery methods for Lock to Live (L2L), a web-based decision aid of safe storage options for firearms and medications.via combinations of email, text, Electronic Health Record (EHR) message, mailed letter, or provider referral, followed by a survey about storage behavior and acceptability. Provider interviews evaluated logistical considerations and acceptability.Patients reporting suicide ideation on the PHQ9 depression screener during outpatient health visits were included. Invitation messages to visit L2L were sent The population-based method reached 2,729 patients and the best method (EHR message plus 2 email reminders) had 11% uptake (L2L visitation rate). Provider referral had small reach (14 patients) and 100% uptake . Provider interviews identified several strategies to promote uptake including: EHR reminders, provider training, quality metrics with accountability, a clearly communicated lethal means screening/counseling policy, and strong organizational leadership support.Despite the low uptake for population-based (11%), far more patients with suicide risk were engaged in the L2L tool through population-based outreach than provider-referral over the same time frame. Reducing access to lethal means for suicide is highly effective for suicide prevention –3. In thThere are several factors contributing to low rates of lethal means counseling particularly within emergency medicine and behavioral health, which include: time-constraints, providers’ lack of perceived benefit for preventing suicide (among emergency medicine providers), lack of training or knowledge of safe storage options, and fear that asking about firearms will damage the patient/clinician relationship , 8. PopuOne challenge to expanding firearm safety interventions delivered by clinicians is whether patients disclose access to firearms. There is disagreement in the literature on this point. Some surveys of firearm owners suggest that most are receptive and understand the importance within the context of suicide prevention , 11. Othhttp://lock2live.org/) (Lock to Live (L2L) was designed with input from over 60 stakeholders, including patients with lived experience  . Lock toperience , medicalperience , which pperience . L2L is Initial testing of L2L with a tablet in an emergency department setting showed that it was highly acceptable across a broad range of provider roles . L2L addresses the lack of provider knowledge for different firearm and medication storage options . A surveoutpatient setting. Reach was defined as the number of patients outreached and uptake was the number of patients who visited L2L. An anonymous follow-up survey evaluated impacts on storage behavior and patient acceptability. Provider interviews evaluated logistical considerations and acceptability.The purpose of this study was to test the feasibility (reach and uptake) and acceptability (patient survey and provider interviews) of delivering the L2L web-based decision aid for safe firearm storage using population-based and provider-referral outreach in the The sample included adult members of a large integrated health care system (Kaiser Permanente Colorado) who reported any frequency of suicidal thoughts on the ninth question of the Patient Health Questionnaire (PHQ-9)  from Octvia different combinations of population-based outreach methods  and direct provider referral. Provider interviews helped characterize the context of current lethal means screening and counseling practices within the health system, logistical considerations, and acceptability of L2L.We used a mixed-methods  embedded design  to assesPopulation-based invitation messages to visit L2L emphasized anonymity and were co-designed with input from clinicians, public health researchers, and members of the Colorado Firearm Safety Coalition. Six combinations of initial + reminder messages  were tested. Letter, email or EHR message were used for initial invites, but never text—due to length restrictions. Combinations of different methods were tested on the hypothesis that people will have varying response propensities to different methods.For the provider referral pilot, nine mental health providers agreed to test L2L with patients for 6 weeks. Patients were selected for L2L referral from clinical judgment of the patient's suicide risk (not just the PHQ-9). Providers had a 1-h orientation to L2L with discussion and agreement on the best referral process. They were provided with scripting templates for introducing L2L. Collaboratively, a goal was set of each provider to refer 10 patients over a 6-week period (providers felt this was easily achievable at the outset). A follow-up discussion with providers identified barriers and facilitators to referral. All study procedures were approved by the Kaiser Permanente Institutional Review Board.Website visit rates assessed uptake of L2L for each outreach method using separate URLs . This allowed tracking of visits associated with different outreach approaches but maintained patient anonymity. Text messages were only used as reminders, but never as initial messages due to length restrictions.All patients received an anonymous survey to evaluate acceptability with L2L and safe storage behavior 4 weeks after their first invitation to visit L2L. Items assessed uptake (visits to website) and satisfaction with L2L, lethal means storage behavior , and whether patients received lethal means counseling from medical or mental health providers. Stage of Change theory  informedvia video conference (Microsoft Teams) and followed an interview guide that assessed current lethal means safety counseling practices, barriers and facilitators for delivering lethal means counseling, and whether and how L2L could be a useful tool for integration either during visits or separately using population-based outreach methods. All interviewees provided informed consent and there was no incentive.The goal of the interviews was to characterize the context of current lethal means screening and counseling practices within the health system, logistical considerations and acceptability of L2L. Eligible providers were mental health therapists, psychiatrists, and primary care providers who responded to email invitations. An invitation email to participate in a 30–45-min interview was sent to a convenience sample of 13 mental health and 10 primary care providers. Interviews were held Defined as all eligible adult patients who endorsed suicide ideation on the PHQ-9 for the population-based approach. Patients were outreached in batches to evaluate the impact of different outreach methods. For the provider-referral approach, reach included all patients who were referred by providers.Uptake was defined by the number of unique IP addresses that visited each unique URL divided by the number of patients sent a message for that batch (outreach method). Email, mail, or EHR messages that bounced back were not included in the denominator. A logistic regression model determined differences in response rates between outreach methods.Descriptive frequencies were calculated for each survey question based on the number of patients who answered that question. Patients who skipped questions or answered “prefer not to answer” were included in the denominator, but those who were never presented with the question because of branching logic or early discontinuation were not counted.via email response.Interviews were audio recorded. Two coders (who conducted the interviews) independently listened to the recordings and noted themes within the apriori topics from the interview guide. The coders then met and shared their ideas and resolved any discrepancies in interpretation. Themes were summarized in a table by the apriori topics and sent to each interviewee for member checking . InterviN = 2,931) was predominantly female (65%) with moderate to severe depression reported on the PHQ-9 .via the EHR; 202 patients (6.8%) were excluded because they were unreachable . Population-based outreach messages were sent to 2,729 of 2,931 adults identified via population-based outreach. In follow-up discussions, providers identified barriers to referral including perceived need for real-time EHR reminders about L2L availability, lack of visit time to bring up L2L, patients declining to learn more about the program, and perception that L2L is better suited for patients with moderate to severe suicide-risk.In the 6-week provider referral pilot, three of the nine outpatient providers referred a total of 14 patients, with a 100% L2L visit rate. One patient (out of 14) had previously received an invitation Of the 326 patients who started the survey, 44 (13.5%) didn't answer any questions, 114 (35.5%) answered some questions, and 168 (51%) completed all questions. Survey completion varied by outreach method . Table 3n = 82/231) had a firearm. Of those with a firearm (N = 82), half  did not endorse any secure storage behaviors such as locking devices or firearm safes. Of those who endorsed at least 1 storage behavior, one person indicated that L2L was influential (N = 41). Most said a friend, family member, or firearms safety class influenced their current storage behavior. Of those with access to a firearm who did not endorse any storage behaviors, 44% (n = 18/41) said they were thinking about firearm storage changes and 31% (n = 14/41) said they were planning changes. A later version of the survey indicated that 50% of those who had access to a firearm and were thinking about storage changes, thought L2L was influential to their considerations .Of those with medications accessible, 36 (27%) were thinking about changes and of those, 57% (20) were planning changes. Disposing of old medications  and medication lockbox  were cited as the most common planned behaviors compared to family/friend managing meds  and receiving smaller prescription amounts . Of those who had medications stored inaccessibly, L2L was cited as influential in 4 cases (6%). In later batches, where we assessed influences on decision making for those considering changes, L2L was cited as the most influential factor, more so than providers, friends/family or other influences. Of the 268 patients who completed, 84 (31%) provided qualitative responses to the open-ended question about barriers to medication storage .Providers said that it was important to highlight the privacy and anonymity of L2L. However, it was emphasized that it would be better not to mention the reason for outreach was their endorsement of suicidal thoughts on the PHQ-9 screening tool. Providers described how patients may be more suspicious of firearm safety information sent I’m not sure it's feasible, but this would be good to send to everyone. There are people out there who aren't getting help; this program might be helpful to them. It would be beneficial to visit the site in-person with the provider. That would eliminate the concern about someone else having accessed their data. A provider can encourage them to participate, even just by giving them a card with the website link.”“Nearly all the providers interviewed felt that L2L was a great resource that provided storage options they didn't know about (such as disassembling a firearm). In general, providers felt that patients would react positively to L2L, that it could promote behavior change and prompt conversations between patients and their care providers. An important consideration was that patients will have to be receptive to lethal means safety steps (beyond pre-contemplative stage of change) before accepting the tool. Many providers felt comfortable addressing patient resistance to the lethal means conversation but were less knowledgeable of different types of storage options.I think L2L could promote behavior change and prompt discussion with providers; it takes away some stigma. Some of my colleagues are uncomfortable having these conversations, and L2L could be helpful in that regard.”“We found that population-based outreach using the Lock to Live (L2L) web-based decision aid for safe storage of lethal means in those with suicide risk reached many patients who had not previously discussed lethal means safety with providers. While uptake to population-based approaches was low (11%) in comparison to provider-referral (100%), most of the low-risk patients outreached using population-based methods had not been previously counseled on lethal means safety per survey report. The absolute number who visited L2L in a 6-week time was 52 for population-based with the best method versus 14 for provider referral. Every patient referred by providers visited L2L, but without more organizational supports , reach will remain low. We learned a great deal from patients and providers about ways to improve uptake of L2L for both self-directed population-based methods and provider referral as part of lethal means counseling.We saw that sending invitations securely through the electronic medical record with two email reminders had the highest L2L visit rate of 11%. EHR messages were the most preferred outreach method based on patient survey feedback and website-usage data. Low-response may be due to stigma often associated with seeking care for mental health conditions . The resvia clinical assessment). Past studies have shown that providers are concerned that lethal means counseling could have a negative impact on the therapeutic relationship  and 50% not considering changes (pre-contemplative). Behavior change interventions that don't incorporate patients readiness for change may be less likely to succeed . ProvideFuture studies should not only measure whether someone with suicide risk has access to a firearm or medication, but whether the current level of suicide risk is consistent with the current storage behavior. Patient survey and provider interview feedback indicated that it may be important to approach changes in firearm storage using a risk-stratification—especially in individuals with low or intermittent suicide risk. Instead of static goals about safe storage, different storage options from less to more inaccessible may be considered based on current risk. Patients with protection weapons (36% of firearm owner respondents) may be more amenable to the idea of temporary off-site storage only during high-risk times, a strategy used by the Gun Shop project . For medIt is important to note limitations to our study that may impact generalizability to other populations. There are many patients who likely didn't respond to the survey or visit L2L due to uneasiness about privacy, even though both were anonymous to address this concern. The invitation message came from the health system and many are skeptical of sharing firearm ownership with medical establishments –12, 29. The goal of the current project was to assess feasibility (reach and uptake) and acceptability (patient survey and provider interviews) of outreach methods to the L2L web-based decision aid. We found that population-based approaches have higher reach and smaller uptake compared to provider referral, but the absolute number reached through population-based approaches was higher. Expanding provider-delivered lethal means safety interventions to all patients reporting suicide risk will require an EHR reminder, provider training, quality metrics with accountability, a clearly communicated lethal means screening/counseling policy, and strong organizational leadership support. Population-based outreach of lethal means safety is a viable low-cost option and may be particularly important tool as a primer to discussion of firearm access with providers.


Protein and mRNA levels correlate only moderately. The availability of proteogenomics data sets with protein and transcript measurements from matching samples is providing new opportunities to assess the degree to which protein levels in a system can be predicted from mRNA information. Here we examined the contributions of input features in protein abundance prediction models. Using large proteogenomics data from 8 cancer types within the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set, we trained models to predict the abundance of over 13,000 proteins using matching transcriptome data from up to 958 tumor or normal adjacent tissue samples each, and compared predictive performances across algorithms, data set sizes, and input features. Over one-third of proteins  showed relatively poor predictability (elastic net r ≤ 0.3) from their cognate transcripts. Moreover, we found widespread occurrences where the abundance of a protein is considerably less well explained by its own cognate transcript level than that of one or more trans locus transcripts. The incorporation of additional trans-locus transcript abundance data as input features increasingly improved the ability to predict sample protein abundance. Transcripts that contribute to non-cognate protein abundance primarily involve those encoding known or predicted interaction partners of the protein of interest, including not only large multi-protein complexes as previously shown, but also small stable complexes in the proteome with only one or few stable interacting partners. Network analysis further shows a complex proteome-wide interdependency of protein abundance on the transcript levels of multiple interacting partners. The predictive model analysis here therefore supports that protein-protein interaction including in small protein complexes exert post-transcriptional influence on proteome compositions more broadly than previously recognized. Moreover, the results suggest mRNA and protein co-expression analysis may have utility for finding gene interactions and predicting expression changes in biological systems. The abundance of mRNA is often measured as a surrogate variable of protein levels, but how well the mRNA level of different genes correlate with their protein across samples remains incompletely understood. Here we trained machine learning models over large RNA sequencing and mass spectrometry data from up to 8 cancer types in the CPTAC data sets to evaluate how well protein level variances across samples can be predicted from their transcripts. Despite voluminous data, up to one-third of genes shows poor mRNA-protein correlation suggesting their protein abundance is not primarily regulated from cognate transcripts. The inclusion of mRNA level information from protein interaction partners into the prediction models substantially improved prediction performance for a subset of genes, suggesting their protein abundance may be primarily regulated post-transcriptionally through protein-protein interactions. Notably, these proteins involve not only subunits of large multi-protein complexes such as the ribosome as previously suspected, but many proteins that form stable interactions with one or few other partners, including the propionyl-CoA carboxylase, mitochondrial calcium uniporter, calcineurin, and others. The results add to emerging evidence of independent regulation of protein levels from their cognate transcripts and suggest avenues to improve the interpretation of transcriptomics data. This has been taken to indicate that while abundant proteins have abundant transcripts, transcript variance within a group of samples does not necessarily predict or signify corresponding protein changes  features, compared to 1 feature in the self transcript model. Incorporation of STRING features led to an average increase in test set correlation coefficients of 0.16 [IQR: 0.07–0.27]  compared to the size of the whole transcriptome feature set, the results indicate that a substantial portion of performance gains over the single-transcript feature set is already achievable from relatively few selected prior features.We next examined more closely the underlying causes behind the performance gains of protein predictions in the CORUM and STRING feature sets. To do so, we examined the genes whose protein prediction performance from transcripts increased substantially (Δr ≥ 0.25) after the incorporation of additional transcript features. In total, we observed 484 such proteins in the CORUM feature sets, and 3,272 proteins from the STRING data set, representing over 24% of all examined proteins. These numbers increase further when a less conservative Δr threshold of ≥ 0.15 is used , altogether suggesting non-cognate transcript contributions to protein level are common at the proteome level. Upon inspecting the model feature coefficients (elastic nets) and feature importance (random forests), we observed that a considerable portion of these proteins with improved prediction are associated with (1) poor contribution from the self-transcript, and (2) a substantial contribution from primarily a few non-cognate  transcripts coding for other proteins (see below). In other words, although model performance continued to increase with increased feature set sizes, the contributions of trans locus transcript features to overall prediction performance is unevenly distributed, and are therefore attributable to a few high contribution genes rather than a simple scaling with feature size. Notably, despite the STRING feature set being substantially larger than the CORUM feature set, substantial contributions from trans locus transcripts in the STRING feature set primarily involve transcripts encoding proteins that form part of the same CORUM complex as the protein of interest itself, suggesting that stable complex memberships play an outsized role in determining protein levels.Fig 3A). PCCB showed poor prediction by their own self-transcripts (r:–0.008) but experienced high gains in test set correlation upon incorporating additional transcript features in the CORUM (r: 0.483) and STRING (r: 0.692) models. CMC1 is an assembly factor that forms an early intermediate of the cytochrome c oxidase complex in the mitochondrion with 14 documented protein members in CORUM . Likewise, CMC1 showed only moderate prediction by their own self-transcripts  but experienced high gains in test set correlation upon incorporating additional transcript features in the CORUM (r: 0.615) and STRING (r: 0.564) models.To illustrate the poor predictive power of cognate transcripts on some proteins, we next interrogated a subset of proteins where a non-self transcript has an outsized effect on the protein level of the proteins in the CORUM and STRING feature sets: Propionyl-CoA Carboxylase Subunit Beta (PCCB), C-X9-C Motif Containing 1 (CMC1), Proteasome Assembly Chaperone 2 (PSMG2), SMCR8-C9orf72 Complex Subunit (SMCR8), Mitochondrial Calcium Uptake 2 (MICU2), and Protein Phosphatase 3 Regulatory Subunit B, Alpha (PPP3R1). PCCB forms the propionyl-CoA carboxylase complex with two protein members, which breaks down certain amino acids in the cell .We then considered the transcriptomics and proteomics data distributions of the features that had highest coefficients in the elastic net model from the CORUM feature set for PCCB and CMC1. For PCCB, most protein-level predictions are recovered when the feature set includes PCCA, which together with PCCB forms the stable propionyl-CoA carboxylase enzyme that consists of six copies of PCCB and six copies of PCCA each. There is a remarkably strong correlation with its propionyl-CoA carboxylase complex interacting partner PCCA at the protein level (r: 0.957), but this correlation is almost entirely absent at the transcript level (r: –0.059). This is partially explainable by the observation that PCCA transcript has a higher level of variability and is also strongly correlated with the PCCB protein level. The total protein level of PCCB is therefore primarily driven by PCCA rather than PCCB transcripts . MT-CO1 is the mitochondrial genome encoded subunit of complex IV that is thought to act as the nascent scaffold around which the complex is assembled. CMC1 binds to MT-CO1 during the formation of the early module known as MITRAC and is subsequently released during assembly [In the case of CMC1, we found that MT-CO1 has the highest contribution to CMC1 protein level  forms the heterodimer PAC1-PAC2 complex (2 protein members in CORUM) along with Proteasome Assembly Chaperone 1 (PSMG1), which binds with 20S proteasome precursors and acts as a scaffold to promote proteasome ring assembly while preventing aberrant dimerization of 20S proteasome α rings [. PSMG2 protein has a higher correlation with PSMG1 transcript (r: 0.472) than its own (r: 0.158). From GTEx v8 data, PSMG2 is expressed more highly than PSMG1 across tissues (TPM ~40 vs. 15) [Two other examples of small stable complexes with interdependent protein and mRNA correlation are shown in  α rings . PSMG2 a vs. 15) , which sipt r: 0.2 than it(C9orf72) and WD Repeat Domain 41 (WDR41) [Fig 5B). A previous study suggested C9orf72 may stabilize excess SMCR8 [In another example, Smith-Magenis Syndrome Chromosomal Region Candidate Gene 8 (SMCR8) forms the heterotrimer C9orf72-SMCR8-WDR41 complex (3 protein members in CORUM) together with the C9orf72-SMCR8 Complex Subunit  (WDR41) . C9orf72 (WDR41) . Mutatioss SMCR8 , providiS5 Fig shows two more examples concerning small complexes. The mitochondrial calcium uniporter complex (MCU complex) contains five protein members in CORUM . Regulatory subunits of the complex, including calcium uptake protein 2, mitochondrial (MICU2) sense calcium levels, regulating the uptake of calcium performed by the mitochondrial calcium uniporter (MCU) [S5A Fig).er (MCU) . Heteroler (MCU) . This is2+ binding regulatory subunit , with 2 isoforms also encoded by 2 separate genes (PPP3R1 and PPP3R2) [S5B Fig). Together, these cases illustrate a general observation––in cases where the protein abundance of a gene is more highly correlated to the transcript level of a binding partner than to its cognate transcript, there appears to be stronger co-expression between the gene and its abundance driver at the protein level but not at the transcript level.The final example highlighted here comes from the heterodimer calcineurin, which is made up of a catalytic subunit , with 3 isoforms encoded by 3 separate genes , and a Ca PPP3R2) . Calcine PPP3R2) . In the S6 Fig). In some examined cases including PSMG2, the transcript coding for the protein of action is more abundant than that of its regulating interacting partner, which is compatible with a scenario where simple stoichiometric constraints control the protein level of the supernumerary subunit. It is however less clear whether this scenario applies to other examined cases including CMC1 and MT-CO1, where the MT-CO1 transcript is much more abundant than CMC1 owing to the multiple copies of mitochondrial genome in the tissue. Hence, how MT-CO1 regulates CMC1 protein level is not explainable directly from transcript stoichiometry alone and awaits further mechanistic studies. Finally, we also examined feature importance in a single data set of a recent CPTAC study that showed the highest performance in the single feature model (LSCC) . Among the six highlighted proteins , five were best predicted by a non-self transcript, and five of the top trans locus predictors were conserved from the combined data set analysis with the exception of MT-CO1, which was not among the model features due to the number of shared observations required. Hence the observed trans locus predictors are conserved in a single data set model and are unlikely to be due to heterogeneity across data sets.To corroborate these observations, we used the Boruta algorithm against the random forest CORUM models and confirmed each of the principal non-cognate transcript features is retained. We further considered Shapley values as an explanation of random forest and gradient boosting models, and likewise found that the interacting protein transcripts formed the top contributors in explaining the abundance of the protein of interest in each of the six cases above . Thus, small protein complexes are widespread in the proteome and a large fraction of the proteome could be placed under complex post-transcriptional control that decouples protein levels from mRNA levels. Moreover, half of the proteins in the CORUM annotations belong to 2 or more distinct complexes (including intermediates and subcomplexes) which suggest further opportunities for more complex patterns of interdependent protein and mRNA levels.Taken together, these results exemplify widespread interdependency of protein levels on trans locus transcripts, involving not only large megadalton protein complexes but many small complexes involved in diverse biological processes in the cell. The CORUM feature set considered here alone contains over 2,700 human complexes derived from CORUM release 3.0 mappable to 3,689 proteins with distinct gene names, with a median of 3 proteins per complex (S6 Table) and the STRING (S7 Table) feature sets. The resulting network is partitioned into connected components of transcript-protein relationships. The subgraph that corresponds to the relationship between PCCA and PCCB as discussed above shows the PCCB node contributing (as transcript) to its own protein level weakly, and the PCCA node contributing strongly to the PCCB node (as protein) . The relationship of CMC1 with other cytochrome c oxidase complex subunits, as described above, is represented in a wheel and spokes pattern where multiple transcripts (orange nodes) contribute either positively or negatively to the CMC1 protein level, with MT-CO1 having the strongest positive contributions . Another member of the MITRAC module COA3 to which CMC1 binds likewise exerts a positive influence on the protein level of CMC1, consistent with the possibility that interdependent protein and transcript relationships may reflect information related to protein complex assembly sequences. Larger subgraphs represent instances where two or more protein target nodes are connected by shared transcript source nodes. Due to the method by which the graphs are generated, protein hubs (with high in-degrees) are more prevalent, but transcripts with high outflow activities are also seen that connect two or more protein nodes. A subgraph in Fig 6C predicts that the transcript levels of SH3KBP1 and CD2AP both contribute to the predicted protein levels of multiple proteins involved in the RICH1/AMOT polarity complex and the PI4K2A-WASH complex, and the protein level of WASHC1 is influenced by genes in the CCC-Wash  complex and the PI4K2A-WASH complex .We next examined whether the interdependency of proteins and trans locus transcripts may be used to infer potential novel regulatory drivers. To do so, we constructed graphical models to visualize the connections between each protein and the transcriptome features that contribute to its predicted level, followed by topological analysis to find hub nodes and extract network patterns. To limit scope, we generated graphs to proteins whose prediction improved in the CPTAC_8 data set following the inclusion of the CORUM or STRING features (Δr ≥ 0.25) A directed graph is then generated using a list of edges that connect feature variables (transcripts) to target variables (proteins), with the edge weights calculated using a function of the elastic net coefficients and random forest feature importance of the models for both the CORUM . Using topological analysis based on the hierarchical community detection framework (HiDeF) method [Fig 6E). We then extracted the hub nodes from the subgraph using the betweenness centrality algorithm, which revealed mitochondrial serine beta-lactamase-like protein (LACTB) to be an upstream gene that is highly connected within the subgraph and exerts a regulatory effect on both small and large mitochondrial ribosome subunit components. Feature importance analysis further suggests the LACTB transcript to have a negative impact on multiple ribosomal mitochondrial proteins . Of note, LACTB was previously identified as a new subunit (MRP-L56) of the mitochondrial ribosomal large subunit isolated from sucrose gradients [LACTB may also affect oxidative phosphorylation in differentiated cells by regulating mitochondrial ribosome biogenesis and protein synthesis, which may be validated experimentally.We highlight an instance where mRNA-protein relationships may be examined to generate new hypotheses. The mitochondrial ribosome is represented in a highly connected subgraph where the abundances of a majority of complex subunit proteins are each contingent upon multiple transcripts  method , we showradients . Subsequradients  that posradients  and thatradients . The phyradients . The anaS8Table) showed chromogranin A and B (CHGA and CHGB) as inward hubs  associated with secreted peptides including CST3, SERPINC1, MFGE8, SERPIND1, and others , which is consistent with the known roles of these acidic glycoproteins as the primary constituents of secretory granules that can regulate their rate of their formation [A similar hub node analysis from the largest subgraph from the STRING feature set  in the CORUM feature set and potentially more transient interactions documented in the STRING feature set. The common presence of small stable complexes in the proteome greatly expands the repertoire of proteins for which due considerations should be given when directly interpreting transcript level data as representing protein level information. Moreover, it has been suggested that promiscuous protein-protein interactions without established biological function may be a common occurrence during the co-evolution of functional protein-protein interactions . The occACTR3 and ARPC4 will be more effective in modulating ARPC3 protein levels than augmenting the expression of ARPC3 itself, which is readily testable by experimentation. More generally, the results here corroborate the importance of post-transcriptional regulation including protein degradation and turnover in modulating protein levels.The imperfect correlation between protein and mRNA points to orthogonal information that exists between transcript and protein regulations, which underpins the untapped potential for further multi-omics integration to derive new insights . The useSignificant differences in protein regulations likely occur in different cell and tissue types. The predictive models here are trained using publicly available CPTAC data from 8 cancer types, which contain transcriptome and proteome data from both tumor and normal adjacent tissue samples. In prior work, we found general concordance between protein and mRNA correlation in CPTAC samples vs. GTEx tissue proteomics data . HoweverWe performed the analysis using expression data from TCGA/CPTAC RNA sequencing experiments through the cptac Python API, which retrieves the final data tables from the flagship CPTAC papers of each individual cancer type . AlthougFinally, pitfalls must be considered when attempting to interpret predictive models in search of mechanistic insights. Feature importance in the predictive models represents correlation rather than causality, and hence interpretations can be confounded by independent confounders, multicollinearity, and reverse causality. In simple cases such as PCCB–PCCA, given the emphasis on prior feature selections that prioritize known protein-protein interaction partners, the logical interpretation would be to assume the hierarchical nature of gene regulations where transcript levels are more likely to affect protein abundance than the opposite, but this assumption becomes more tenuous as feature sets expand and the number of associations increase, and in cases such as transcription factor proteins whose abundance can affect transcript levels across samples. Future work in this area may employ more sophisticated causal inference methods to identify regulatory modalities.In summary, this study compared predictive models of protein levels using different transcript feature sets, and provided biological interpretations of the results by highlighting trans locus transcripts with substantial contributions to protein levels. The analysis here therefore reveals new details into the gene identity and modality of trans regulation of protein levels, and gives support to further development of prior transcript feature selection strategies to optimize protein prediction tasks. The results show that the transcript levels of protein-protein interaction partners can broadly influence protein abundance in a tissue, which has implications on the interpretations of transcriptomics data and on understanding the architecture of proteome composition regulations. With further refinement of feature selection and feature engineering methods and the availability of large data sets, we foresee that similar approaches to those shown here will provide valuable new insights into post-transcriptional mechanisms of protein regulations.S1 FigA. Box plots of test set normalized root mean square error (NRMSE) between the transcript-predicted and actual protein level for each protein are shown across five feature sets  and three algorithms . In each plot, x axis denotes the number of CPTAC data set used to train the models box: interquartile range; whiskers: +/– 1.5 IQR. B. As above, but for test set goodness-of-fit (R2).(PDF)Click here for additional data file.S2 FigA. Model performance in single cancer data sets. Box plots of test set correlation coefficients (r) between the transcript-predicted and actual protein level for each protein are shown across five feature sets  and three algorithms . In each plot, x axis denotes the CPTAC cancer type study used to train the models; box: interquartile range; whiskers: +/– 1.5 IQR. B. Model performance when the 8 data sets were combined in the order of decreasing single data set performance.(PDF)Click here for additional data file.S3 FigA–B. Scatterplot showing a significant linear relationship between the number of low-confidence interactors a protein has as annotated in STRING vs. the increase in test set correlation between predicted vs. actual protein levels in the Elastic Net models over the self-transcript feature set . C–D. As above, but for the CORUM feature set.(PDF)Click here for additional data file.S4 FigA. MICU2, B. PPP3R1. For each protein, the transcript-trained prediction of protein level is plotted on the x axis and the actual protein level is plotted on the y axis. Blue: train set, brown: test set. Columns denote the transcript feature set used to train the model. The number of features used to train the model in each feature set is shown inside each plot. r: Correlation coefficient.Two additional highlighted proteins with substantial predictability from transcriptome data upon the inclusion of additional features are shown: (PDF)Click here for additional data file.S5 FigA. MICU2 protein level is predicted by MCU transcript but not its own transcript. B. PPP3R1 protein level is explained by PPP3CA transcript level but not its own transcript. Substantial correlations across transcripts and proteins (≥ 0.4) are bolded.Two examples of proteins whose abundance is better explained by another transcript are shown. (PDF)Click here for additional data file.S6 FigA. random forest and B. gradient boosting model output from the CPTAC_8 CORUM feature set. The SHAP values of top transcript features and their impact on model output are shown for each of six proteins highlighted in the text (from top to bottom): PCCB, CMC1, PSMG2, SMCR8, MICU2, PPP3R1.SHAP interpretation of feature importance in (PDF)Click here for additional data file.S7 FigConsistent with the analysis of the combined CPTAC_8 data set, five of the six proteins are not best predicted by their cognate transcripts and all but one (MT-CO1) of the top trans locus transcripts are preserved, which was not among the examined candidate features. Only test set data points are shown.(PDF)Click here for additional data file.S8 FigNames of select complexes are labeled. The majority of complexes are small with a median of 3 proteins per complex.(PDF)Click here for additional data file.S9 FigThe hierarchical network diagram shows Chromogranin A and B (CHGA/CHGB) (magenta) and their first-degree inward flow neighbors (green) in the largest subgraph constructed from the STRING data set. The protein level of CHGA and CHGB is associated with the transcript levels of multiple secreted peptide coding genes, consistent with these proteins serve to regulate gene expression in secretion pathways.(PDF)Click here for additional data file.S1 Table(TSV)Click here for additional data file.S2 Table(TSV)Click here for additional data file.S3 Table(TSV)Click here for additional data file.S4 Table(TSV)Click here for additional data file.S5 Table(TSV)Click here for additional data file.S6 Table(CSV)Click here for additional data file.S7 Table(CSV)Click here for additional data file.S8 Table(TXT)Click here for additional data file.


The presented method was developed for high-density point clouds, like those from unmanned aerial system imagery, to extract and model individual tree location, height, and diameter at breast height (DBH). Extraction of tree locations and heights uses a variable window function searching point cloud-derived canopy height models. Tree DBH is extracted for a subset of point cloud trees using a slice at 1.32-1.42 m and a least-squares circle fitting algorithm. Extracted heights and DBHs are spatially matched and filtered against each tree's expected DBH predicted using a regional National Forest Inventory height to DBH relationship. Values remaining after filtering are used to create a site-specific height to DBH relationship for predicting missing DBH values. Applying the method in a ponderosa pine-dominated forest found that extracted height values exceeded the precision of field height measurement approaches, while the accuracy of extracted and modeled DBH values had a mean error of 0.79 cm.  Specifications tableThe demand for spatially explicit observations of tree heights and diameters at breast height  has been increasing for assessment of forest management objectives For over three decades, airborne laser scanning (ALS) has been the prominent technology for acquiring data points describing the vertical and horizontal heterogeneity of forest structure −2in situ tree observation.The integration of Structure from Motion (SfM) photogrammetry with UAS acquired high-resolution imagery can produce data densities exceeding 1,000 points mPinus ponderosa Lawson & C. Lawson) dominated site at the Manitou Experimental Forest in the Pike-San Isabel National Forest of Colorado, USA. The forests at the site are an all-age ponderosa pine stand, characterized by a mosaic of spatially aggregated tree groups where each group generally contains a single cohort of tree sizes Development of the method occurred in a ponderosa pine  UAS SfM-derived point cloud from Swayze et al. This method uses three stages, including 1) data collection and image processing, 2) tree height and DBH extraction, and 3) DBH filtering and modeling . The metUAS-SfM point clouds underwent a series of steps to prepare them for tree metric extraction, including 1) ground classification, 2) height normalization A, and 3)Following the generation of 10 cm CHMs, extraction of individual tree locations and heights was completed using a local maximum variable window function from the R ForestTools package After individual tree height extraction, the point cloud is processed to extract DBH measurements using the R TreeLS package Extraction of tree metrics from the point cloud resulted in two datasets, each containing coordinates for either the tree heights or DBHs A. AlthouFirst, to aid in filtering any possible outlier DBH values, regional forest inventory data is used to develop a height to DBH relationship to filter matched UAS DBH and height values. The original implementation of this method drew upon the United States Forest Inventory and Analysis (FIA) program Second, an extracted tree DBH was targeted and matched with extracted heights and their corresponding regionally predicted DBH values within a 4 m radius B. The abth percentile bounds of the regional tree height to DBH relationship (Third, the matched height and DBH values were filtered to remove DBHs outside of the 90tionship B. Filtertionship .Fig. 6Ma2 values between UAS and stem mapped DBH values to exceed 0.75, significantly improving previous methods [This method was initially presented and validated by Swayze et al.  methods ,29.Within Swayze et al.