Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis.

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data provided new insights into the understanding of epigenetic heterogeneity and transcriptional regulation. With the increasing abundance of dataset resources, there is an urgent need to extract more useful information through high-quality data analysis methods specifically designed for scATAC-seq. However, analyzing scATAC-seq data poses challenges due to its near binarization, high sparsity and ultra-high dimensionality properties. Here, we proposed a novel network diffusion-based computational method to comprehensively analyze scATAC-seq data, named Single-Cell ATAC-seq Analysis via Network Refinement with Peaks Location Information (SCARP). SCARP formulates the Network Refinement diffusion method under the graph theory framework to aggregate information from different network orders, effectively compensating for missing signals in the scATAC-seq data. By incorporating distance information between adjacent peaks on the genome, SCARP also contributes to depicting the co-accessibility of peaks. These two innovations empower SCARP to obtain lower-dimensional representations for both cells and peaks more effectively. We have demonstrated through sufficient experiments that SCARP facilitated superior analyses of scATAC-seq data. Specifically, SCARP exhibited outstanding cell clustering performance, enabling better elucidation of cell heterogeneity and the discovery of new biologically significant cell subpopulations. Additionally, SCARP was also instrumental in portraying co-accessibility relationships of accessible regions and providing new insight into transcriptional regulation. Consequently, SCARP identified genes that were involved in key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diseases and predicted reliable cis-regulatory interactions. To sum up, our studies suggested that SCARP is a promising tool to comprehensively analyze the scATAC-seq data.

Zebrafish and idiopathic scoliosis: the 'unknown knowns'.

The etiology and heterogeneity of idiopathic scoliosis (IS) are poorly understood. Studies using scoliotic zebrafish models have indicated a potential link between ciliary defects and scoliosis. They may further explain the onset of IS partially. However, it is necessary to further interpret the link between this progress and clinical medicine.

Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks.

MOTIVATION: Gene regulatory networks (GRNs) are vital tools for delineating regulatory relationships between transcription factors and their target genes. The boom in computational biology and various biotechnologies has made inferring GRNs from multi-omics data a hot topic. However, when networks are constructed from gene expression data, they often suffer from false-positive problem due to the transitive effects of correlation. The presence of spurious noise edges obscures the real gene interactions, which makes downstream analyses, such as detecting gene function modules and predicting disease-related genes, difficult and inefficient. Therefore, there is an urgent and compelling need to develop network denoising methods to improve the accuracy of GRN inference. RESULTS: In this study, we proposed a novel network denoising method named REverse Network Diffusion On Random walks (RENDOR). RENDOR is designed to enhance the accuracy of GRNs afflicted by indirect effects. RENDOR takes noisy networks as input, models higher-order indirect interactions between genes by transitive closure, eliminates false-positive effects using the inverse network diffusion method, and produces refined networks as output. We conducted a comparative assessment of GRN inference accuracy before and after denoising on simulated networks and real GRNs. Our results emphasized that the network derived from RENDOR more accurately and effectively captures gene interactions. This study demonstrates the significance of removing network indirect noise and highlights the effectiveness of the proposed method in enhancing the signal-to-noise ratio of noisy networks. AVAILABILITY AND IMPLEMENTATION: The R package RENDOR is provided at https://github.com/Wu-Lab/RENDOR and other source code and data are available at https://github.com/Wu-Lab/RENDOR-reproduce.

The non-cell-autonomous function of ID1 promotes AML progression via ANGPTL7 from the microenvironment.

The bone marrow microenvironment (BMM) can regulate leukemia stem cells (LSCs) via secreted factors. Increasing evidence suggests that dissecting the mechanisms by which the BMM maintains LSCs may lead to the development of effective therapies for the eradication of leukemia. Inhibitor of DNA binding 1 (ID1), a key transcriptional regulator in LSCs, previously identified by us, controls cytokine production in the BMM, but the role of ID1 in acute myeloid leukemia (AML) BMM remains obscure. Here, we report that ID1 is highly expressed in the BMM of patients with AML, especially in BM mesenchymal stem cells, and that the high expression of ID1 in the AML BMM is induced by BMP6, secreted from AML cells. Knocking out ID1 in mesenchymal cells significantly suppresses the proliferation of cocultured AML cells. Loss of Id1 in the BMM results in impaired AML progression in AML mouse models. Mechanistically, we found that Id1 deficiency significantly reduces SP1 protein levels in mesenchymal cells cocultured with AML cells. Using ID1-interactome analysis, we found that ID1 interacts with RNF4, an E3 ubiquitin ligase, and causes a decrease in SP1 ubiquitination. Disrupting the ID1-RNF4 interaction via truncation in mesenchymal cells significantly reduces SP1 protein levels and delays AML cell proliferation. We identify that the target of Sp1, Angptl7, is the primary differentially expression protein factor in Id1-deficient BM supernatant fluid to regulate AML progression in mice. Our study highlights the critical role of ID1 in the AML BMM and aids the development of therapeutic strategies for AML.

Dual-field-of-view high-spectral-resolution lidar: Simultaneous profiling of aerosol and water cloud to study aerosol-cloud interaction.

SignificanceAerosol-cloud interaction affects the cooling of Earth's climate, mostly by activation of aerosols as cloud condensation nuclei that can increase the amount of sunlight reflected back to space. But the controlling physical processes remain uncertain in current climate models. We present a lidar-based technique as a unique remote-sensing tool without thermodynamic assumptions for simultaneously profiling diurnal aerosol and water cloud properties with high resolution. Direct lateral observations of cloud properties show that the vertical structure of low-level water clouds can be far from being perfectly adiabatic. Furthermore, our analysis reveals that, instead of an increase of liquid water path (LWP) as proposed by most general circulation models, elevated aerosol loading can cause a net decrease in LWP.

Interaction-based transcriptome analysis via differential network inference.

Gene-based transcriptome analysis, such as differential expression analysis, can identify the key factors causing disease production, cell differentiation and other biological processes. However, this is not enough because basic life activities are mainly driven by the interactions between genes. Although there have been already many differential network inference methods for identifying the differential gene interactions, currently, most studies still only use the information of nodes in the network for downstream analyses. To investigate the insight into differential gene interactions, we should perform interaction-based transcriptome analysis (IBTA) instead of gene-based analysis after obtaining the differential networks. In this paper, we illustrated a workflow of IBTA by developing a Co-hub Differential Network inference (CDN) algorithm, and a novel interaction-based metric, pivot APC2. We confirmed the superior performance of CDN through simulation experiments compared with other popular differential network inference algorithms. Furthermore, three case studies are given using colorectal cancer, COVID-19 and triple-negative breast cancer datasets to demonstrate the ability of our interaction-based analytical process to uncover causative mechanisms.

hDirect-MAP: projection-free single-cell modeling of response to checkpoint immunotherapy.

There is a lack of robust generalizable predictive biomarkers of response to immune checkpoint blockade in multiple types of cancer. We develop hDirect-MAP, an algorithm that maps T cells into a shared high-dimensional (HD) expression space of diverse T cell functional signatures in which cells group by the common T cell phenotypes rather than dimensional reduced features or a distorted view of these features. Using projection-free single-cell modeling, hDirect-MAP first removed a large group of cells that did not contribute to response and then clearly distinguished T cells into response-specific subpopulations that were defined by critical T cell functional markers of strong differential expression patterns. We found that these grouped cells cannot be distinguished by dimensional-reduction algorithms but are blended by diluted expression patterns. Moreover, these identified response-specific T cell subpopulations enabled a generalizable prediction by their HD metrics. Tested using five single-cell RNA-seq or mass cytometry datasets from basal cell carcinoma, squamous cell carcinoma and melanoma, hDirect-MAP demonstrated common response-specific T cell phenotypes that defined a generalizable and accurate predictive biomarker.

Dynamics of SARS-CoV-2 IgG antibodies and neutralising antibodies in rheumatic and musculoskeletal diseases patients with COVID-19.

OBJECTIVES: Rheumatic and musculoskeletal diseases (RMD) may exhibit different immune responses to novel coronavirus (COVID-19) infection compared to healthy individuals. While previous studies have primarily investigated changes in COVID-19-related antibodies post-vaccination for RMD patients, this study sought to explore the dynamics of SARS-CoV-2 IgG antibodies and neutralising antibodies (NAb) in RMD patients after COVID-19 infection. METHODS: In this longitudinal study, we monitored the SARS-CoV-2 IgG antibodies and NAb levels in RMD patients and healthy controls (HC) at 60 and 90 days post-COVID-19 infection. Chemiluminescent immunoassay was used to detect the levels of novel coronavirus-specific IgG (anti-S1/S2 IgG) antibodies and NAb. RESULTS: A total of 292 RMD patients and 104 HC were enrolled in the study. At both the 60-day and 90-day post-COVID-19 infection, RMD patients exhibited significantly lower levels of anti-S1/S2 IgG and NAb than those in the HC group (p<0.001). The anti-S1/S2 IgG antibody levels remained relatively stable, while the NAb levels in RMD patients could vary greatly between the 60th and 90th days. A logistic regression analysis revealed that the prior administration of glucocorticoids (GC), immunosuppressants, and b/tsDMARDs stood out as independent risk factors associated with reduced anti-S1/S2 IgG and NAb levels, irrespective of the specific RMD subtypes. CONCLUSIONS: GC and anti-rheumatic medications can potentially alter the production of specific antibodies, especially NAb, in RMD patients post-COVID-19 infection. These findings emphasise the importance of continuous monitoring for NAb fluctuations in RMD patients following a COVID-19 infection.

Raman lidar at 355 nm using low dead time photon counting for atmospheric aerosol measurements.

Photon counting is an effective way to enhance the dynamic range of the data acquisition system (DAQ) in Raman lidars. However, there exists a deficiency of relatively high dead times among current options, which necessitates an additional calibration procedure for the nonlinearity of the photon counting signal, thus leading to unanticipated errors. A field programmable gate array (FPGA)-based photon counting module has been proposed and implemented in a Raman lidar, offering two operational channels. Through observational experiments, it was determined that this module has an overall dead time of 1.13 ns taking advantage of the high-speed amplifier/discriminator pair and the logic design, a significant improvement compared to the 4.35 ns of a commercially used Licel transient recorder within the same counting rate range. This notably low dead time implies that its output maintains sufficient linearity even at substantially high counting rates. As a result, the need for a dead time calibration procedure prior to signal integration with the analog signal is eliminated, reducing uncertainty in the final integrated signal, and even in the retrieval result. The backscattering result of the comparison between this module and a transient recorder indicates that a more precise performance can be acquired benefiting from this hardware upgrading.

The Structural and Molecular Mechanisms of Mycobacterium tuberculosis Translational Elongation Factor Proteins.

Targeting translation factor proteins holds promise for developing innovative anti-tuberculosis drugs. During protein translation, many factors cause ribosomes to stall at messenger RNA (mRNA). To maintain protein homeostasis, bacteria have evolved various ribosome rescue mechanisms, including the predominant trans-translation process, to release stalled ribosomes and remove aberrant mRNAs. The rescue systems require the participation of translation elongation factor proteins (EFs) and are essential for bacterial physiology and reproduction. However, they disappear during eukaryotic evolution, which makes the essential proteins and translation elongation factors promising antimicrobial drug targets. Here, we review the structural and molecular mechanisms of the translation elongation factors EF-Tu, EF-Ts, and EF-G, which play essential roles in the normal translation and ribosome rescue mechanisms of Mycobacterium tuberculosis (Mtb). We also briefly describe the structure-based, computer-assisted study of anti-tuberculosis drugs.

PathExpSurv: pathway expansion for explainable survival analysis and disease gene discovery.

BACKGROUND: In the field of biology and medicine, the interpretability and accuracy are both important when designing predictive models. The interpretability of many machine learning models such as neural networks is still a challenge. Recently, many researchers utilized prior information such as biological pathways to develop neural networks-based methods, so as to provide some insights and interpretability for the models. However, the prior biological knowledge may be incomplete and there still exists some unknown information to be explored. RESULTS: We proposed a novel method, named PathExpSurv, to gain an insight into the black-box model of neural network for cancer survival analysis. We demonstrated that PathExpSurv could not only incorporate the known prior information into the model, but also explore the unknown possible expansion to the existing pathways. We performed downstream analyses based on the expanded pathways and successfully identified some key genes associated with the diseases and original pathways. CONCLUSIONS: Our proposed PathExpSurv is a novel, effective and interpretable method for survival analysis. It has great utility and value in medical diagnosis and offers a promising framework for biological research.

Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs.

MOTIVATION: Differential network inference is a fundamental and challenging problem to reveal gene interactions and regulation relationships under different conditions. Many algorithms have been developed for this problem; however, they do not consider the differences between the importance of genes, which may not fit the real-world situation. Different genes have different mutation probabilities, and the vital genes associated with basic life activities have less fault tolerance to mutation. Equally treating all genes may bias the results of differential network inference. Thus, it is necessary to consider the importance of genes in the models of differential network inference. RESULTS: Based on the Gaussian graphical model with adaptive gene importance regularization, we develop a novel Importance-Penalized Joint Graphical Lasso method (IPJGL) for differential network inference. The presented method is validated by the simulation experiments as well as the real datasets. Furthermore, to precisely evaluate the results of differential network inference, we propose a new metric named APC2 for the differential levels of gene pairs. We apply IPJGL to analyze the TCGA colorectal and breast cancer datasets and find some candidate cancer genes with significant survival analysis results, including SOST for colorectal cancer and RBBP8 for breast cancer. We also conduct further analysis based on the interactions in the Reactome database and confirm the utility of our method. AVAILABILITY AND IMPLEMENTATION: R source code of Importance-Penalized Joint Graphical Lasso is freely available at https://github.com/Wu-Lab/IPJGL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

P38-DAPK1 axis regulated LC3-associated phagocytosis (LAP) of microglia in an in vitro subarachnoid hemorrhage model.

BACKGROUND: The phagocytosis and homeostasis of microglia play an important role in promoting blood clearance and improving prognosis after subarachnoid hemorrhage (SAH). LC3-assocaited phagocytosis (LAP) contributes to the microglial phagocytosis and homeostasis via autophagy-related components. With RNA-seq sequencing, we found potential signal pathways and genes which were important for the LAP of microglia. METHODS: We used an in vitro model of oxyhemoglobin exposure as SAH model in the study. RNA-seq sequencing was performed to seek critical signal pathways and genes in regulating LAP. Bioparticles were used to access the phagocytic ability of microglia. Western blot (WB), immunoprecipitation, quantitative polymerase chain reaction (qPCR) and immunofluorescence were performed to detect the expression change of LAP-related components and investigate the potential mechanisms. RESULTS: In vitro SAH model, there were increased inflammation and decreased phagocytosis in microglia. At the same time, we found that the LAP of microglia was inhibited in all stages. RNA-seq sequencing revealed the importance of P38 MAPK signal pathway and DAPK1 in regulating microglial LAP. P38 was found to regulate the expression of DAPK1, and P38-DAPK1 axis was identified to regulate the LAP and homeostasis of microglia after SAH. Finally, we found that P38-DAPK1 axis regulated expression of BECN1, which indicated the potential mechanism of P38-DAPK1 axis regulating microglial LAP. CONCLUSION: P38-DAPK1 axis regulated the LAP of microglia via BECN1, affecting the phagocytosis and homeostasis of microglia in vitro SAH model. Video Abstract.

Potential of Mie-Fluorescence-Raman Lidar to Profile Chlorophyll a Concentration in Inland Waters.

Vertical distribution of phytoplankton is crucial for assessing the trophic status and primary production in inland waters. However, there is sparse information about phytoplankton vertical distribution due to the lack of sufficient measurements. Here, we report, to the best of our knowledge, the first Mie-fluorescence-Raman lidar (MFRL) measurements of continuous chlorophyll a (Chl-a) profiles as well as their parametrization in inland water. The lidar-measured Chl-a during several experiments showed good agreement with the in situ data. A case study verified that MFRL had the potential to profile the Chl-a concentration. The results revealed that the maintenance of subsurface chlorophyll maxima (SCM) was influenced by light and nutrient inputs. Furthermore, inspired by the observations from MFRL, an SCM model built upon surface Chl-a concentration and euphotic layer depth was proposed with root mean square relative difference of 16.5% compared to MFRL observations, providing the possibility to map 3D Chl-a distribution in aquatic ecosystems by integrated active-passive remote sensing technology. Profiling and modeling Chl-a concentration with MFRL are expected to be of paramount importance for monitoring inland water ecosystems and environments.

Deficiency of cystathionine gamma-lyase promotes aortic elastolysis and medial degeneration in aged mice.

Enzymatic degradation of elastin by matrix metalloproteinases (MMPs) leads to the permanent dilation of aortic wall and constitutes the most prominent characters of aortic aneurysm and aging-related medial degeneration. Hydrogen sulfide (H2S) as a gasotransmitter exhibits a wide variety of cardio-protective functions through its anti-inflammatory and anti-oxidative actions. Cystathionine gamma-lyase (CSE) is a main H2S-generating enzyme in cardiovascular system. The regulatory roles of CSE/H2S system on elastin homeostasis and blood vessel degeneration have not yet been explored. Here we found that aged CSE knockout mice had severe aortic dilation and elastic degradation in abdominal aorta and were more sensitive to angiotensin II-induced aortic elastolysis and medial degeneration. Administration of NaHS would protect the mice from angiotensin II-induced inflammation, gelatinolytic activity, elastin fragmentation, and aortic dilation. In addition, human aortic aneurysm samples had higher inflammatory infiltration and lower expression of CSE. In cultured smooth muscle cells (SMCs), TNFα-induced MMP2/9 hyperactivity and elastolysis could be attenuated by exogenously applied NaHS or CSE overexpression while further deteriorated by complete knockout of CSE. It was further found that H2S inhibited MMP2 transcription by posttranslational modification of Sp1 via S-sulfhydration. H2S also directly suppressed MMP hyperactivity by S-sulfhydrating the cysteine switch motif. Taken together, this study revealed the involvement of CSE/H2S system in the pathogenesis of aortic elastolysis and medial degeneration by maintaining the inactive form of MMPs, suggesting that CSE/H2S system can be a target for the prevention of age-related medial degeneration and treatment of aortic aneurysm.

Effect of hydrogen sulfide on glycolysis-based energy production in mouse erythrocytes.

Hydrogen sulfide (H2 S) is a gasotransmitter that regulates both physiological and pathophysiological processes in mammalian cells. Recent studies have demonstrated that H2 S promotes aerobic energy production in the mitochondria in response to hypoxia, but its effect on anaerobic energy production has yet to be established. Glycolysis is the anaerobic process by which ATP is produced through the metabolism of glucose. Mammalian red blood cells (RBCs) extrude mitochondria and nucleus during erythropoiesis. These cells would serve as a unique model to observe the effect of H2 S on glycolysis-mediated energy production. The purpose of this study was to determine the effect of H2 S on glycolysis-mediated energy production in mitochondria-free mouse RBCs. Western blot analysis showed that the only H2 S-generating enzyme expressed in mouse RBCs is 3-mercaptopyruvate sulfurtransferase (MST). Supplement of the substrate for MST stimulated, but the inhibition of the same suppressed, the endogenous production of H2 S. Both exogenously administered H2 S salt and MST-derived endogenous H2 S stimulated glycolysis-mediated ATP production. The effect of NaHS on ATP levels was not affected by oxygenation status. On the contrary, hypoxia increased intracellular H2 S levels and MST activity in mouse RBCs. The mitochondria-targeted H2 S donor, AP39, did not affect ATP levels of mouse RBCs. NaHS at low concentrations (3-100 µM) increased ATP levels and decreased cell viability after 3 days of incubation in vitro. Higher NaHS concentrations (300-1000 µM) lowered ATP levels, but prolonged cell viability. H2 S may offer a cytoprotective effect in mammalian RBCs to maintain oxygen-independent energy production.

Experimental demonstration of spectral suppression effect improvement for multi-longitudinal mode high-spectral-resolution lidar.

The multi-longitudinal mode high-spectral-resolution lidar (MLM-HSRL) is an effective technique for detecting atmospheric optical characteristics of aerosols. Due to the excessive longitudinal mode numbers, the current MLM-HSRL cannot obtain a well spectral suppression effect, which seriously affects the retrieval accuracy of the optical characteristic parameters. In this paper, a new index called Longitudinal Mode Rejection Ratio (LMRR) has been proposed to evaluate the spectral suppression effect of the MLM-HSRL; a novel mismatch error and mode control (MEMC) technique is proposed to improve the spectral suppression effect of the MLM-HSRL, which contributes to developing the scientific potential of the MLM-HSRL for aerosol remote sensing. Based on our self-developed MLM laser, through controlling the longitudinal mode frequency-pulled shift of the MLM laser, adjusting the total mismatch error, and reducing the longitudinal mode numbers, we realize the LMRR index improved from about 5 to over 30, and the working stability of the system is also promoted by decreasing the longitudinal mode numbers. The experiment well improves the spectral suppression effect and verifies the effectiveness of the proposed MEMC technique. To the best of our knowledge, for the first time, the study addresses the conundrum of the lower spectral suppression effect for the MLM-HSRL. This work would help to provide a powerful support for the high-precision, long-term, and stable operation of the MLM-HSRL in the future.

High-spectral-resolution LIDAR based on a few-longitudinal mode laser for aerosol and cloud characteristics detection.

A novel implementation of high-spectral-resolution LIDAR based on a passively Q-switched few-longitudinal mode laser (PQFLM-HSRL) is proposed, and the prototype is built for detecting aerosol and cloud characteristics. The spatial-temporal distributions of the aerosol and cloud are continuously observed by the PQFLM-HSRL for the first time, to the best of our knowledge. Based on observation, we present the retrieval results of backscatter coefficient, particle linear depolarization ratio, and LIDAR ratio, and these intensive parameters are used to classify the aerosol and cloud into different types. Particularly, we have observed mix-phased clouds. The resulting aerosol optical depths (AODs) are highly consistent with CE-318, the Sun photometer measurements of the local National Meteorological Station (NMS), which verify the retrieval accuracy and the system stability. In addition, the retrieved AODs also characterize the ambient air quality, which show a high correlation with the measured PM2.5 concentrations. The implementation of the PQFLM-HSRL provides a new method for atmospheric feature detection, which shows superior scientific potential for further study on climate change and environmental health.

Cystathionine gamma-lyase/H2 S signaling facilitates myogenesis under aging and injury condition.

Hydrogen sulfide (H2 S) can be endogenously produced and belongs to the class of signaling molecules known as gasotransmitters. Cystathionine gamma-lyase (CSE)-derived H2 S is implicated in the regulation of cell differentiation and the aging process, but the involvements of the CSE/H2 S system in myogenesis upon aging and injury have not been explored. In this study, we demonstrated that CSE acts as a major H2 S-generating enzyme in skeletal muscles and is significantly down-regulated in aged skeletal muscles in mice. CSE deficiency exacerbated the age-dependent sarcopenia and cardiotoxin-induced injury/regeneration in mouse skeletal muscle, possibly attributed to inefficient myogenesis. In contrast, supplement of NaHS (an H2 S donor) induced the expressions of myogenic genes and promoted muscle regeneration in mice. In vitro, incubation of myoblast cells (C2C12) with H2 S promoted myogenesis, as evidenced by the inhibition of cell cycle progression and migration, altered expressions of myogenic markers, elongation of myoblasts, and formation of multinucleated myotubes. Myogenesis was also found to upregulate CSE expression, while blockage of CSE/H2 S signaling resulted in a suppression of myogenesis. Mechanically, H2 S significantly induced the heterodimer formation between MEF2c and MRF4 and promoted the binding of MEF2c/MRF4 to myogenin promoter. MEF2c was S-sulfhydrated at both cysteine 361 and 420 in the C-terminal transactivation domain, and blockage of MEF2c S-sulfhydration abolished the stimulatory role of H2 S on MEF2c/MRF4 heterodimer formation. These findings support an essential role for H2 S in maintaining myogenesis, presenting it as a potential candidate for the prevention of age-related sarcopenia and treatment of muscle injury.

Validation of the dual field-of-view polarization LIDAR technique for the retrieval of homogeneous water cloud microphysical properties: a study based on a polarimetric Monte Carlo simulation.

This article highlights the validation of the dual fields-of-view (FOVs) polarization lidar technique for the retrieval of cloud droplet effective radius in conjunction with cloud extinction coefficient of homogeneous water cloud via simulation approach. The simulation is based on polarimetric Monte Carlo method incorporated with semianalytic features under multiple-scattering conditions. The simulation results show that the depolarization ratio measured at dual-FOVs is a function of the cloud droplet effective radius and cloud extinction coefficient. Using the method of standard deviation on extensive simulation results and then by applying the polynomial regression, two polynomial relationships are obtained expressing the retrieval of the cloud droplet effective radius and cloud extinction coefficient from the layer integrated depolarization ratio at low optical depths close to the cloud bottom. Eventually, the results those presented by Ref.[1] are validated. The water cloud microphysical properties, liquid water content and cloud droplet number concentration are the functions of these two parameters and thus can be found numerically.