iATMEcell: identification of abnormal tumor microenvironment cells to predict the clinical outcomes in cancer based on cell-cell crosstalk network.

Interactions between Tumor microenvironment (TME) cells shape the unique growth environment, sustaining tumor growth and causing the immune escape of tumor cells. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the identification of cancer-related TME cells. Here, we proposed a novel network-based computational method, named as iATMEcell, to identify the abnormal TME cells associated with the biological outcome of interest based on a cell-cell crosstalk network. In the method, iATMEcell first manually collected TME cell types from multiple published studies and obtained their corresponding gene signatures. Then, a weighted cell-cell crosstalk network was constructed in the context of a specific cancer bulk tissue transcriptome data, where the weight between cells reflects both their biological function similarity and the transcriptional dysregulated activities of gene signatures shared by them. Finally, it used a network propagation algorithm to identify significantly dysregulated TME cells. Using the cancer genome atlas (TCGA) Bladder Urothelial Carcinoma training set and two independent validation sets, we illustrated that iATMEcell could identify significant abnormal cells associated with patient survival and immunotherapy response. iATMEcell was further applied to a pan-cancer analysis, which revealed that four common abnormal immune cells play important roles in the patient prognosis across multiple cancer types. Collectively, we demonstrated that iATMEcell could identify potentially abnormal TME cells based on a cell-cell crosstalk network, which provided a new insight into understanding the effect of TME cells in cancer. iATMEcell is developed as an R package, which is freely available on GitHub (https://github.com/hanjunwei-lab/iATMEcell).

Deep-Cloud: A Deep Neural Network-Based Approach for Analyzing Differentially Expressed Genes of RNA-seq Data.

Presently, the field of analyzing differentially expressed genes (DEGs) of RNA-seq data is still in its infancy, with new approaches constantly being proposed. Taking advantage of deep neural networks to explore gene expression information on RNA-seq data can provide a novel possibility in the biomedical field. In this study, a novel approach based on a deep learning algorithm and cloud model was developed, named Deep-Cloud. Its main advantage is not only using a convolutional neural network and long short-term memory to extract original data features and estimate gene expression of RNA-seq data but also combining the statistical method of the cloud model to quantify the uncertainty and carry out in-depth analysis of the DEGs between the disease groups and the control groups. Compared with traditional analysis software of DEGs, the Deep-cloud model further improves the sensitivity and accuracy of obtaining DEGs from RNA-seq data. Overall, the proposed new approach Deep-cloud paves a new pathway for mining RNA-seq data in the biomedical field.

Prediction of Transcription Factor Binding Sites on Cell-Free DNA Based on Deep Learning.

Transcription factors (TFs) are important regulatory elements for vital cellular activities, and the identification of transcription factor binding sites (TFBS) can help to explore gene regulatory mechanisms. Research studies have proved that cfDNA (cell-free DNA) shows relatively higher coverage at TFBS due to the protection by TF from degradation by nucleases and short fragments of cfDNA are enriched in TFBS. However, there are still great difficulties in the noninvasive identification of TFBSs from experimental techniques. In this study, we propose a deep learning-based approach that can noninvasively predict TFBSs of cfDNA by learning sequence information from known TFBSs through convolutional neural networks. Under the addition of long short-term memory, our model achieved an area under the curve of 84%. Based on this model to predict cfDNA, we found consistent motifs in cfDNA fragments and lower coverage occurred upstream and downstream of these cfDNA fragments, which is consistent with a previous study. We also found that the binding sites of the same TF differ in different cell lines. TF-specific target genes were detected from cfDNA and were enriched in cancer-related pathways. In summary, our method of locating TFBSs from plasma has the potential to reflect the intrinsic regulatory mechanism from a noninvasive perspective and provide technical guidance for dynamic monitoring of disease in clinical practice.

Mutational profiling of circulating tumor DNA and clinical characteristics in lymphoma: Based on next generation sequencing.

Liquid biopsy has been experimented with to identify the mutation of lymphoma based on next-generation sequencing (NGS). We applied NGS analysis to circulating tumor DNA (ctDNA) in 20 lymphoma patients. Then, we compared treatment outcomes, and clinical characteristics among these patients, then investigated mutational profiling. Two independent cohorts of 241 patients with mature B cell lymphoma in Mature B-cell malignancies data set (MBN) data set and 50 diffuse large B-cell lymphoma (DLBCL) patients in DLBCL data set, were used to examine the association between gene mutations and prognosis. We found ctDNA positive group had significantly more relapsed/PD (7/12, 58.3%) and less CR/PR patients (1/12, 8.3%) compared to negative group (0, 0%) (5/8, 62.5%) (p < 0.001). Somatic alterations were identified in 12 of 20 patients and the total 11 mutations were: Ataxia telangiectasia mutated (ATM), TP53, BCL2, BTG2, CD28, EP300, IDH2, IRF8, JAK3, NOTCH1, and NRAS. ATM (S2168L) was found in SLL and TLBL for the first time. BTG2 (c.292_293del), CD28 (P119T), IRF8 (E74D) and NOTCH1 (c.4348 G > A) were newly detected in DLBCL, angioimmunoblastic T-cell lymphoma, primary central nervous system lymphoma, and BCL for the first time respectively. We also disclosed an unreported mutation EP300 (c.1058_1059insC) in DLBCL. Our cases implied ctDNA detection consistent with the FISH of tissue samples to some extent, speculating new molecular subtypes of DLBCL, finding some potential drug-resistant mutations, and suggesting disease recurrence. Moreover, in MBN and DLBCL datasets, patients with TP53 mutation had a significantly shorter OS (all p < 0.05) in both circulating free DNA and tumor tissue. The mutations (no SNP) of NOTCH1 (all p < 0.05) significantly contributed to worse OS in the two cohorts.

A comprehensive characterization of cell-free RNA in spent blastocyst medium and quality prediction for blastocyst.

The rate of pregnancy can be affected by many factors in assisted reproductive technology (ART), and one of which is the quality of embryos. Therefore, selecting the embryos with high potential is crucial for the outcome. Fifteen spent blastocyst medium (SBM) samples were collected from 14 patients who received in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI), seven from high-grade embryos and eight from low-grade embryos. Cell-free RNA (cf-RNA) profile of SBM samples were analyzed by RNA sequencing in the present study. It was found that a large amount of cf-RNA were released into SBM, including protein-coding genes (68.9%) and long noncoding RNAs (lncRNAs) (17.26%). Furthermore, a high correlation was observed between blastocyst genes and SBM genes. And the cf-mRNAs of SBM were highly fragmented, and coding sequence (CDS) and untranslated (UTR) regions were released equally. Two hundred and thirty-two differentially expressed genes were identified in high-grade SBM (hSBM) and low-grade SBM (lSBM), which could be potential biomarker in distinguishing the embryos with different quality as an alternative or supplementary approach for subjective morphology criteria. Hence, cf-RNAs sequencing revealed the characterization of circulating transcriptomes of embryos with different quality. Based on the results, the genes related to blastocyst quality were screened, including the genes closely related to translation, immune-signaling pathway, and amino acid metabolism. Overall, the present study showed the types of SBM cf-RNAs, and the integrated analysis of cf-RNAs profiling with morphology grading displayed its potential in predicting blastocyst quality. The present study provided valuable scientific basis for noninvasive embryo selection in ART by RNA-profiling analysis.

Cell-Free DNA Fragmentomics: The Novel Promising Biomarker.

Cell-free DNA molecules are released into the plasma via apoptotic or necrotic events and active release mechanisms, which carry the genetic and epigenetic information of its origin tissues. However, cfDNA is the mixture of various cell fragments, and the efficient enrichment of cfDNA fragments with diagnostic value remains a great challenge for application in the clinical setting. Evidence from recent years shows that cfDNA fragmentomics' characteristics differ in normal and diseased individuals without the need to distinguish the source of the cfDNA fragments, which makes it a promising novel biomarker. Moreover, cfDNA fragmentomics can identify tissue origins by inferring epigenetic information. Thus, further insights into the fragmentomics of plasma cfDNA shed light on the origin and fragmentation mechanisms of cfDNA during physiological and pathological processes in diseases and enhance our ability to take the advantage of plasma cfDNA as a molecular diagnostic tool. In this review, we focus on the cfDNA fragment characteristics and its potential application, such as fragment length, end motifs, jagged ends, preferred end coordinates, as well as nucleosome footprints, open chromatin region, and gene expression inferred by the cfDNA fragmentation pattern across the genome. Furthermore, we summarize the methods for deducing the tissue of origin by cfDNA fragmentomics.

Spatial Transcriptomic Analysis Reveals Regional Transcript Changes in Early and Late Stages of rd1 Model Mice with Retinitis Pigmentosa.

Retinitis pigmentosa (RP) is the leading cause of inherited blindness with a genetically heterogeneous disorder. Currently, there is no effective treatment that can protect vision for those with RP. In recent decades, the rd1 mouse has been used to study the pathological mechanisms of RP. Molecular biological studies using rd1 mice have clarified the mechanism of the apoptosis of photoreceptor cells in the early stage of RP. However, the pathological changes in RP over time remain unclear. The unknown pathology mechanism of RP over time and the difficulty of clinical treatment make it urgent to perform more refined and spatially informed molecular biology studies of RP. In this study, spatial transcriptomic analysis is used to study the changes in different retinal layers of rd1 mice at different ages. The results demonstrate the pattern of photoreceptor apoptosis between rd1 mice and the control group. Not only was oxidative stress enhanced in the late stage of RP, but it was accompanied by an up-regulation of the VEGF pathway. Analysis of temporal kinetic trends has further identified patterns of changes in the key pathways of the early and late stages, to help understand the important pathogenesis of RP. Overall, the application of spatial transcriptomics to rd1 mice can help to elucidate the important pathogenesis of RP involving photoreceptor apoptosis and retinal remodeling.

Single-Nucleus RNA-Seq: Open the Era of Great Navigation for FFPE Tissue.

Single-cell sequencing (scRNA-seq) has revolutionized our ability to explore heterogeneity and genetic variations at the single-cell level, opening up new avenues for understanding disease mechanisms and cell-cell interactions. Single-nucleus RNA-sequencing (snRNA-seq) is emerging as a promising solution to scRNA-seq due to its reduced ionized transcription bias and compatibility with richer samples. This approach will provide an exciting opportunity for in-depth exploration of billions of formalin-fixed paraffin-embedded (FFPE) tissues. Recent advancements in single-cell/nucleus gene expression workflows tailored for FFPE tissues have demonstrated their feasibility and provided crucial guidance for future studies utilizing FFPE specimens. In this review, we provide a broad overview of the nuclear preparation strategies, the latest technologies of snRNA-seq applicable to FFPE samples. Finally, the limitations and potential technical developments of snRNA-seq in FFPE samples are summarized. The development of snRNA-seq technologies for FFPE samples will lay a foundation for transcriptomic studies of valuable samples in clinical medicine and human sample banks.

Spatial Transcriptome Profiling of Mouse Hippocampal Single Cell Microzone in Parkinson's Disease.

The hippocampus is an important part of the limbic system in the human brain that has essential roles in spatial navigation and cognitive functions. It is still unknown how gene expression changes in single-cell in different spatial locations of the hippocampus of Parkinson's disease. The purpose of this study was to analyze the gene expression features of single cells in different spatial locations of mouse hippocampus, and to explore the effects of gene expression regulation on learning and memory mechanisms. Here, we obtained 74 single-cell samples from different spatial locations in a mouse hippocampus through microdissection technology, and used single-cell RNA-sequencing and spatial transcriptome sequencing to visualize and quantify the single-cell transcriptome features of tissue sections. The results of differential expression analysis showed that the expression of Sv2b, Neurod6, Grp and Stk32b genes in a hippocampus single cell at different locations was significantly different, and the marker genes of CA1, CA3 and DG subregions were identified. The results of gene function enrichment analysis showed that the up-regulated differentially expressed genes Tubb2a, Eno1, Atp2b1, Plk2, Map4, Pex5l, Fibcd1 and Pdzd2 were mainly involved in neuron to neuron synapse, vesicle-mediated transport in synapse, calcium signaling pathway and neurodegenerative disease pathways, thus affecting learning and memory function. It revealed the transcriptome profile and heterogeneity of spatially located cells in the hippocampus of PD for the first time, and demonstrated that the impaired learning and memory ability of PD was affected by the synergistic effect of CA1 and CA3 subregions neuron genes. These results are crucial for understanding the pathological mechanism of the Parkinson's disease and making precise treatment plans.

[Imputation method for dropout in single-cell transcriptome data].

Single-cell transcriptome sequencing (scRNA-seq) can resolve the expression characteristics of cells in tissues with single-cell precision, enabling researchers to quantify cellular heterogeneity within populations with higher resolution, revealing potentially heterogeneous cell populations and the dynamics of complex tissues. However, the presence of a large number of technical zeros in scRNA-seq data will have an impact on downstream analysis of cell clustering, differential genes, cell annotation, and pseudotime, hindering the discovery of meaningful biological signals. The main idea to solve this problem is to make use of the potential correlation between cells and genes, and to impute the technical zeros through the observed data. Based on this, this paper reviewed the basic methods of imputing technical zeros in the scRNA-seq data and discussed the advantages and disadvantages of the existing methods. Finally, recommendations and perspectives on the use and development of the method were provided.

Solid-state nanopore analysis on the conformation change of DNA polymerase I induced by a DNA substrate.

Proteins with a changeable conformation, such as polymerases, play a very important role in various life activities. Their conformational changes can be reflected in their structural size and flexibility, which may influence their transport kinetics. Recently, solid-state nanopore sensors have been widely applied to characterize the conformation of proteins and other complex structures as sensitive and high throughput single-molecule detectors. In this work, we used a SiN nanopore sensor to study the conformational changes between the Klenow fragment (KF) and its monomer complex with a DNA substrate (KF-DNA). By calculating their hydrodynamic radii, pore volume, the duration of translocation events, drift velocity, and molecular dynamics simulations, we found that the KF-DNA monomer complex has a tighter structure and transports slower. The study performed here can be potentially used to identify single polymerases in real time and may ultimately reveal conformation changes and the interaction between polymerases and their substrates.

Ultrasensitive detection of multiple Alzheimer's disease biomarkers by SERS-LFA.

Alzheimer's disease (AD) is one of the top public health crises in the 21st century, especially in an aging society. Early diagnosis, prevention, and intervention can significantly reduce the risk of AD. Detection of multiple AD biomarkers in blood is an effective strategy and has drawn more and more attention in recent years. However, the concentration of AD biomarkers is very low, therefore, point-of-care testing (POCT) techniques are needed for sensitive detection. Herein, a lateral flow assay, based on Surface-enhanced Raman scattering nanotags (SERS-LFA), is proposed for the simultaneous quantification of multiple AD biomarkers including Amyloid-beta 42, Amyloid-beta 40, tau proteins, and neurofilament light chain. The limit of detection for four AD biomarkers is 138.1, 191.2, 257.1, and 309.1 fg mL-1, respectively, which are two orders of magnitude lower than their concentrations in blood. Compared with the existing detection technology, SERS-LFA has the advantages of high specificity, high sensitivity, low cost, multiple detection, and rapid detection. Therefore, SERS-LFA has a broad application prospect in the early diagnosis and monitoring of AD in the future.

Comparative analysis of circular RNA enrichment methods.

The circRNAs sequencing results vary due to the different enrichment methods and their performance is needed to systematic comparison. This study investigated the effects of different circRNA enrichment methods on sequencing results, including abundance and species of circRNAs, as well as the sensitivity and precision. This experiment was carried out by following four common circRNA enrichment methods: including ribosomal RNA depletion (rRNA-), polyadenylation and poly (A+) RNA depletion followed by RNase R treatment (polyA+RNase R), rRNA-+polyA+RNase R and polyA+RNase R+ rRNA-. The results showed that polyA+RNase R+ rRNA - enrichment method obtained more circRNA number, higher sensitivity and abundance among them; polyA+RNase R method obtained higher precision. The linear RNAs can be thoroughly removed in all enrichment methods except rRNA depletion method. Overall, our results helps researchers to quickly selection a circRNA enrichment of suitable for own study among many enrichment methods, and it provides a benchmark framework for future improvements circRNA enrichment methods.[Figure: see text].

Associations of air PM2.5 level with gut microbiota in Chinese Han preschoolers and effect modification by oxytocin receptor gene polymorphism.

Variations in the single nucleotide polymorphisms (SNPs) of the oxytocin receptors (OXTR) indicate individual differences in stress response, social behavior, and psychopathology, but very few paper mentioned OXTR as part of the mechanism linking exposure to air pollution and poor social interactions. The authors investigated the moderating role of Oxytocin receptor (OXTR) rs53576 polymorphism in the relationship between PM2.5 level and gut microbiota in children, in an attempt to provide some reference for the evidence linking biological and environmental factors to children brain development. The study included 86 healthy Chinese preschoolers (50 males, 36 females) from two campuses of a kindergarten with different air PM2.5 levels. Atmospheric PM2.5 values released by air quality monitoring stations where the two campuses are located were collected for 30 days. The genotypes of OXTR rs53576 were determined by PCR and restriction fragment length polymorphism. The gut microbiota situation was evaluated by determining urinary concentrations of short-chain fatty acids. Urinary levels of cortisone and cortisol were determined to assess the impact of air pollution on the HPA axis. Urinary 2'-Deoxy-7,8-dihydro-8-oxoguanosine and 8-oxo-7,8-dihydroguanosine were measured to evaluate the oxidative stress state. The genotype distribution frequency of rs53576 polymorphism was consistent with Hardy-Weinberg equilibrium. The average urinary concentrations of cortisone, cortisol and 8-OHdG in high pollution campus preschoolers were significantly higher than those in low pollution campus preschoolers, while situations were opposite for acetic acid, propionic acid, isobutyric acid, butyric acid and valeric acid. The interaction between OXTR rs53576 and air pollution had a significant effect on urinary acetic acid. Allele G of rs53576 may be a risk factor for gut microbiota disorder caused by air pollution, and children with GA/GG genotype may be more susceptible than those with AA genotype.

Prediction of Time-Series Transcriptomic Gene Expression Based on Long Short-Term Memory with Empirical Mode Decomposition.

RNA degradation can significantly affect the results of gene expression profiling, with subsequent analysis failing to faithfully represent the initial gene expression level. It is urgent to have an artificial intelligence approach to better utilize the limited data to obtain meaningful and reliable analysis results in the case of data with missing destination time. In this study, we propose a method based on the signal decomposition technique and deep learning, named Multi-LSTM. It is divided into two main modules: One decomposes the collected gene expression data by an empirical mode decomposition (EMD) algorithm to obtain a series of sub-modules with different frequencies to improve data stability and reduce modeling complexity. The other is based on long short-term memory (LSTM) as the core predictor, aiming to deeply explore the temporal nonlinear relationships embedded in the sub-modules. Finally, the prediction results of sub-modules are reconstructed to obtain the final prediction results of time-series transcriptomic gene expression. The results show that EMD can efficiently reduce the nonlinearity of the original data, which provides reliable theoretical support to reduce the complexity and improve the robustness of LSTM models. Overall, the decomposition-combination prediction framework can effectively predict gene expression levels at unknown time points.

Defining Specific Cell States of MPTP-Induced Parkinson's Disease by Single-Nucleus RNA Sequencing.

Parkinson's disease (PD) is a neurodegenerative disease with an impairment of movement execution that is related to age and genetic and environmental factors. 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is a neurotoxin widely used to induce PD models, but the effect of MPTP on the cells and genes of PD has not been fully elucidated. By single-nucleus RNA sequencing, we uncovered the PD-specific cells and revealed the changes in their cellular states, including astrocytosis and endothelial cells' absence, as well as a cluster of medium spiny neuron cells unique to PD. Furthermore, trajectory analysis of astrocyte and endothelial cell populations predicted candidate target gene sets that might be associated with PD. Notably, the detailed regulatory roles of astrocyte-specific transcription factors Dbx2 and Sox13 in PD were revealed in our work. Finally, we characterized the cell-cell communications of PD-specific cells and found that the overall communication strength was enhanced in PD compared with a matched control, especially the signaling pathways of NRXN and NEGR. Our work provides an overview of the changes in cellular states of the MPTP-induced mouse brain.

Optimization of library preparation based on SMART for ultralow RNA-seq in mice brain tissues.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) provides new insights to address biological and medical questions, and it will benefit more from the ultralow input RNA or subcellular sequencing. RESULTS: Here, we present a highly sensitive library construction protocol for ultralow input RNA sequencing (ulRNA-seq). We systematically evaluate experimental conditions of this protocol, such as reverse transcriptase, template-switching oligos (TSO), and template RNA structure. It was found that Maxima H Minus reverse transcriptase and rN modified TSO, as well as all RNA templates capped with m7G improved the sequencing sensitivity and low abundance gene detection ability. RNA-seq libraries were successfully prepared from total RNA samples as low as 0.5 pg, and more than 2000 genes have been identified. CONCLUSIONS: The ability of low abundance gene detection and sensitivity were largely enhanced with this optimized protocol. It was also confirmed in single-cell sequencing, that more genes and cell markers were identified compared to conventional sequencing method. We expect that ulRNA-seq will sequence and transcriptome characterization for the subcellular of disease tissue, to find the corresponding treatment plan.

Identification of full-length circular nucleic acids using long-read sequencing technologies.

Unlike the traditional perception in genomic DNA or linear RNA, circular nucleic acids are a class of functional biomolecules with a circular configuration and are often observed in nature. These circular molecules encompass the full spectrum of size and play an important role in organisms, making circular nucleic acids research worthy. Due to the low abundance of most types of circular nucleic acids and the disadvantages of short-read sequencing platforms, accurate and full-length circular nucleic acid sequencing and identification is difficult. In this review, we have provided insights into full-length circular nucleic acid detection methods using long-read sequencing technologies, with a focus on the experimental and bioinformatics strategies to obtain accurate sequences.

Low bias multiple displacement amplification with confinement effect based on agarose gel.

Multiple displacement amplification (MDA) is a popular single-cell whole-genome amplification (WGA) technique that can greatly improve the amplification efficiency of single-cell genomes. However, there is an inherent problem that cannot be completely solved, that is, the amplification bias. We here propose an improved MDA method based on low melting agarose gel, named gelMDA. Firstly, the agarose gel and solution were characterized with SEM and fluorescent reagent. Then, we used gelMDA for cDNA amplification in library preparation of RNA-seq, and conventional MDA was used as a comparison. The sensitivity, efficiency of gelMDA, and amplification bias were evaluated with fluorescence curve, product yield, and the sequencing results. Finally, gelMDA was used for single-cell transcriptome sequencing. The results showed that the sensitivity and product yield of gelMDA were significantly higher than those of conventional MDA. A lower coefficient of variation (CV) and a higher reproducibility were obtained from gelMDA sequencing results. A region of 30 µm in diameter was amplified from the tissue sections and successfully sequenced. In conclusion, gelMDA obtained higher amplification efficiency and lower amplification bias in the present study. It suggested the great potential in single-cell RNA amplification and sequencing.

The integrity of cfDNA in follicular fluid and spent medium from embryo culture is associated with embryo grade in patients undergoing in vitro fertilization.

PURPOSE: This study was conducted to verify if the cfDNA integrity (cfDI) in follicular fluid and subsequent spent embryo medium (SEM) could serve as potential non-invasive biomarker for high-grade embryo selection during IVF/ICSI. METHODS: Thirty-two follicular fluids, 32 subsequent corresponding cleavage embryo SEM, and 23 subsequent blastocyst SEM were collected from 11 patients undergoing IVF/ICSI. CfDI was measured by ALU gene amplicons with different sizes by qPCR, as the ratio of long to short fragments. RESULTS: CfDI in follicular fluid corresponding to subsequent high-grade cleavage embryos and blastocysts was significantly lower than that related to low-grade embryos (p = 0.018). Conversely, cfDI in SEM was significantly and positively correlated with high-grade embryos at both stages (p = 0.009). ROC curves of the analysis of cfDI in follicular fluid showed great potential in predicting subsequent embryogenesis and embryo grade (AUC > 0.927). Regardless of the cleavage embryo grade by morphology, cfDI in day 3 SEM could predict if the cleavage embryo could develop to a high-grade blastocyst (AUC = 0.820). A concordant shift pattern of cfDI from follicular fluid to subsequent day 3 SEM and day 5 SEM was found in 81.82% participants featured by various clinical characteristics. CONCLUSION: CfDI in follicular fluid and SEM was significantly correlated with embryogenesis and embryo grade and could serve as a potential non-invasive biomarker in high-grade embryo selection. Direct qPCR was proved as a labor-saving and sensitive method for the analysis of cfDI in low volume of SEM.