Would you eat lab-grown pork to save the environment? – PHL17 Philadelphia

Ray Copley, Manager of Media and Cell Line Development at Clever Carnivore, looks at cells growing in a nutrient-rich medium. Courtesy of Clever Carnivore.

CHICAGO (NewsNation) The process of growing meat made in a lab from a single animal cell looks a lot like making cotton candy.

Tiny white flecks spin in a red liquid feeding their growth, clumping together over time until theres enough muscle tissue to create a steak, chicken nuggets or, in the case of Clever Carnivore, a Chicago-style sausage.

Its full circle: (Chicagos history) being the meat packing capital, the Stockyards, and bringing that back in a sustainable, eco-friendly, cruelty-free way, said Ray Copley, a biomedical engineer who manages the startups cell line development.

Clever Carnivore is one of a handful of startups working to grow meat that is identical on a cellular level to farm-raised meat without the need to raise livestock.

Proponents say that compared to traditional meat, this cultivated meat could cut down greenhouse gas emissions by 87%, and reduce supply chain volatility that has wreaked havoc on food prices in recent years. It uses less land, water and energy to produce.

It may also be an appealing solution for people who want to eat more sustainably but arent willing to give up meat.

If cultivated meat doesnt taste the same, cook the same and come in at the same price point we know (it) can never convert the majority of consumers, said Virginia Ramos, who founded the company with her husband Paul Burridge.

While the advent of test-tube meat might seem like a scene out of The Hitchhikers Guide to the Galaxy, such a future is already here. Diners in Singapore have been able to taste cultivated chicken in high-end restaurants since 2020.

In the U.S., the Food and Drug Administration just approved a second brand of lab-grown chicken for the market. However, its manufacture is likely too expensive for it to show up in the average grocery store just yet.

Tyson one of the largest meat producers in the world has contributed funding to Clever Carnivore, which is currently working to start the process for FDA approval. The organizations goal is to offer consumers a $1 sausage comparable in price and substance on the market in early 2025.

Such breakthroughs are the pinnacle of a decadelong movement to find an alternative to the traditional way a pork chop makes its way to your corner store. The global market for meat substitutes has grown by 48% annually since 2013, according to data analyzed by Statista.

Still, a lot of people continue to prefer meat $1.3 trillion worth this year the analysis found. By comparison, alternative meat solutions are predicted to make $13 billion globally.

Clever Carnivores method is similar to that used in stem cell research, an area in which Burridge has more than a decade of expertise.

Scientists take a small skin biopsy from an adult pig, then turn it back into an embryonic-like stem cell through a process called pluripotency. This cell is then placed in a nutrient-rich liquid and stored at a pigs internal body temperature while the cells replicate.

When there are enough cells, theyre then put in a bioreactor a machine that gently spins, causing the replicating muscle cells to clump together. Once the cells reach a specific mass, they can be combined with animal or plant fat, spices or flavorings to create essentially a biological replica of what you see at a grocery store.

But not all cultivated meat is created equal, Ramos said. Some companies use genetic modification, add growth hormones or include animal-derived ingredients which may not be palatable to consumers. Others use methods that contain both skin and muscle cells in the final product, impacting the flavor.

Clever Carnivore stresses that Burridges expertise allows them to avoid all that, more or less just kind of recreating the basic conditions necessary to sustain the cell growth, basically creating the conditions inside a pig, outside of a pig, Ramos said.

And those arent the only challenges facing companies trying to perfect this new fare.

Some experts point to companies using more energy and disposable plastic than expected, throwing doubts on the industrys ability to substantially reduce climate change effects. Others say theres still a lot we dont know about the long-term effects of growing muscle tissue so quickly.

But despite the unknowns, many say cultivated meat will revolutionize how Americans think about the question, Where does my food come from?

Everything is identified, everything that the cells have consumed or been exposed to, all the way from the original animal, Copley said.

Continued here:
Would you eat lab-grown pork to save the environment? - PHL17 Philadelphia

Base editing of SMN2 gene restores production of SMN protein, curing spinal muscular atrophy in mice – Medical Xpress

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(A) Immunofluorescence images of spinal cord sections from wild-type 7SMA mice at 25 weeks that received AAV9-ABE + AAV9-GFP in a 10:1 ratio by neonatal ICV injection, stained for GFP to indicate AAV transduction, NeuN as a marker of post-mitotic neurons, and DAPI to stain all nuclei. (B) In vivo base editing conversion of C6T in the spinal cord of 7SMA mice treated with AAV9-ABE + AAV9-GFP in bulk dissociated tissue, and GFP+ enriched nuclei. (C) CIRCLE-Seq nominations of candidate off-target sites in NIH3T3 cell genomic DNA treated in vitro with purified Spy-mac nuclease and P8 sgRNA. Mismatches at each off-target locus are shown relative to the sgRNA above. (D) On-target and off-target base editing of strategy D10 in 7SMA mESCs. Bars show editing of the highest edited nucleotide (P# shown in parenthesis) at each locus. (E) Fluorescence imaging of CND and MND differentiated 7SMAmESCs that harbor the Mnx1:GFP reporter of motor neurons and stably integrated with the D10 ABE strategy. (F) RT-qPCR for ABE8e expression in 7SMAmESCs (n=3) and differentiated MND (n=3) and CND (n=3) populations, previously transfected with the D10 strategy. (G) Gene expression analysis of 7SMAmESCs (n=3), and CND (n=3) and MND (n=3) differentiated cells showing expression levels of various motor neuron specific, neuron specific, spinal cord patterning, glia, and embryonic stem cell markers. Credit: Science (2023). DOI: 10.1126/science.adg6518

A team of medical researchers affiliated with a host of institutions in the U.S. has used base editing to restore the natural production of the SMN protein in mice, effectively curing spinal muscular atrophy (SMA) in the rodents. In their paper published in the journal Science, the group describes their base editing approach and its performance in restoring natural SMN production in mice afflicted with SMA.

SMA is one of the leading causes of infant mortality in humans. Babies born with the condition have a mutation in the SMN1 gene, resulting in production of insufficient amounts of the protein SMN, leading to neural deterioration and death. Many babies born with the condition who are diagnosed early enough are given drugs to increase production of SMN artificially, which slows progression of the disease, but cannot stop it completely. Thus, other therapies are needed.

In this new effort, the researchers used base editing, a kind of gene editing that is done chemically, to treat the disease in mice. Base editing is typically used to make single-nucleotide changes in a genome, as was done in this case. A recording of David R. Liu's lecture on base editing and prime editing. Credit: David R. Liu

In this particular instance, the change was made to the SMN2 gene, which normally partially encodes for production of SMNthe changes the team made fully activated the gene, allowing for more production of SMN. The SMN2 gene is related to the SMN1 gene, but there is an important difference: SMN2 has a C6>T mutation that makes it unable to regulate SMN protein production. Altering the mutation in a way that made it identical to the unmutated SMN1 gene removed this restriction, allowing the gene to encode for unlimited amounts of SMN.

Close monitoring of the altered mice showed the base editing restored production of SMN to normal levels, preventing neural degeneration. They also found that in cases where degeneration had already occurred, base editing led to regeneration and improved motor function. They also found that editing the mice's genes increased lifespan from an average of just 17 days (for a control group) to more than 100 days.

More information: Mandana Arbab et al, Base editing rescue of spinal muscular atrophy in cells and in mice, Science (2023). DOI: 10.1126/science.adg6518

Journal information: Science

2023 Science X Network

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Base editing of SMN2 gene restores production of SMN protein, curing spinal muscular atrophy in mice - Medical Xpress

Spatial multiomics map of trophoblast development in early pregnancy – Nature.com

Human samples

Placental and decidual samples used for the in vivo and in vitro profiling were obtained from elective terminations from: The MRC and Wellcome-funded Human Developmental Biology Resource (HDBR, https://www.hdbr.org), with appropriate maternal written consent and approval from the Fulham Research Ethics Committee (REC reference 18/LO/0822) and Newcastle and North Tyneside 1 Research Ethics Committee (REC reference 18/NE/0290). The HDBR is regulated by the UK Human Tissue Authority (HTA; https://www.hta.gov.uk) and operates in accordance with the relevant HTA Codes of Practice.Addenbookes Hospital (Cambridge) under ethical approval from the Cambridge Local Research Ethics Committee (04/Q0108/23), which is incorporated into the overarching ethics permission given to the Centre for Trophoblast Research biobank for the Biology of the Human Uterus in Pregnancy and Disease Tissue Bank at the University of Cambridge under ethical approval from the East of England-Cambridge Central Research Ethics Committee (17/EE/0151) and from the London-Hampstead Research Ethics Committee (20/LO/0115).

Placentaldecidual blocks (P13, P14 and P34) were collected prior to 1 September 2006 and consent for research use was not obtained. These samples are considered Existing Holdings under the Human Tissue Act and as such were able to be used in this project. All the other tissue samples used for this study were obtained with written informed consent from all participants in accordance with the guidelines in The Declaration of Helsinki 2000.

All samples profiled were histologically normal.

TSC lines BTS5 and BTS11 derived from human blastocysts by H. Okae and colleagues5 were used in this study. Informed consent was obtained from all donors prior to the establishment of the cell line and the study was approved by the Ethics Committee of Tohoku University School of Medicine (Research license 2016-1-371), associated hospitals, the Japan Society of Obstetrics and Gynecology and the Ministry of Education, Culture, Sports, Science and Technology (Japan). This work was internally approved by HuMFre-20-0005 at the Wellcome Sanger Institute and the lines were covered by a Conditions of Use agreement with the Tohoku University School of Medicine (internal reference CG175).

Fresh tissue samples of human implantation sites were embedded in cold OCT medium and flash-frozen using a dry ice-isopentane slurry as described46.

Quality of archival frozen tissue samples was assessed by extraction of RNA from cryosections using the QIAGEN RNeasy Mini Kit, according to the manufacturers instructions including on-column DNase I digestion. RNA quality was assayed using the Agilent RNA 6000 Nano Kit. All samples processed for Visium and single-nuclei had RIN values greater than 8.7.

Single-nuclei suspensions were isolated from frozen tissue sections when performing multiomic snRNA-seq, scATAC-seq and snRNA-seq, following the manufacturers instructions. For each OCT-embedded sample, 400m of tissue was prepared as 50m cryosections, which were paused in a tube on dry ice until subsequent processing. Nuclei were released via Dounce homogenization as described47.

We used the previous protocol optimized for the decidualplacental interface13. In short, decidual tissues were enzymatically digested in 15ml 0.4mgml1 collagenase V (Sigma, C9263) solution in RPMI 1640 medium (Thermo Fisher Scientific, 21875-034)/10% FCS (Biosfera, FB-1001) at 37C for 45min. The supernatant was diluted with medium and passed through a 100-m cell sieve (Corning, 431752) and then a 40-m cell sieve (Corning, 431750). The flow-through was centrifuged and resuspended in 5ml of red blood cell lysis buffer (Invitrogen, 00-4300) for 10min. Placental villi were scraped from the chorionic membrane using a scalpel and the stripped membrane was discarded. The resultant villous tissue was enzymatically digested in 70ml 0.2% trypsin 250 (Pan Biotech P10-025100P)/0.02% EDTA (Sigma E9884) in PBS with stirring at 37C for 9min. The disaggregated cell suspension was diluted with medium and passed through a 100-m cell sieve (Corning, 431752). The undigested gelatinous tissue remnant was retrieved from the gauze and further digested with 1015ml collagenase V at 1.0mgml1 (Sigma C9263) in Hams F12 medium/10% FBS with gentle shaking at 37C for 10min. The disaggregated cell suspension was diluted with medium and passed through a 100m cell sieve (Corning, 431752). Cells obtained from both enzyme digests were pooled together and passed through a 100m cell sieve (Corning, 431752) and washed in Hams F12. The flow-through was centrifuged and resuspended in 5ml of red blood cell lysis buffer (Invitrogen, 00-4300) for 10min.

Trophoblast stem cell (TSC) lines BTS5 and BTS11 derived by Okae and colleagues were grown as described previously5. In brief, TSC self-renewing medium (TSCM) components were substituted with local suppliers with the exception for 30% w/v BSA from WAKO Japan and CHIR99021 concentration was increased to 6M which maintained the undifferentiated morphology as well as preserving its EVT invasive morphology. TSCs were grown on 5gml1 Collagen IV (Corning) coated wells and early passaged cells between passages 24 and 26 were used for differentiation and analysis. For 2D differentiation into EVT identity, cells were seeded at a density of 1.3105 per cm2 (corresponding to 125,000 cells plated on a well of a 6-well plate) in EVTM1 detailed below supplemented with ice-cold 2% Matrigel GFR (Corning) before seeding on 1gml1 Collagen IV (Corning) coated wells (D0). Three days later (D3), medium was changed to EVTM2 supplemented with ice-cold 0.5% Matrigel GFR. Three days later (D6), the medium was changed to EVT medium 3 supplemented with ice-cold 0.5% Matrigel GFR. Cells were treated with TrypLE for downstream analysis 48h later (D8). For CXCL16 induction experiments, a final concentration of 100ngml1 CXCL16 (RnD 976-CX-025 with carrier, dissolved in 0.1%BSA(WAKO)/PBS) were supplemented to EVTM2 or EVTM3 and analysed 48h later. The induction was controlled by supplementing an equal volume of 0.1% BSA/PBS.

In total, six trophoblast organoids were grown and differentiated into EVT as previously described3,48. To differentiate trophoblast organoids into EVT, organoids were cultured with TOM for ~34 days and transferred into EVTM1 (+NRG1) for ~47 days. Once trophoblasts initiate their commitment into EVT (spike emergence), EVTM2 (NRG1) is added for 4 days. Donors were differentiated and collected in batches of three that were multiplexed on the same 10x Genomics reaction. Samples for donors 1, 2 and 3 were collected at 3h, 24h and 48h after the addition of EVTM2, while samples for donors 4, 5 and 6 were collected at 48h before, and then 0h, 48h and 96h after, addition of EVTM2. Organoids grown in TOM were also collected as a control at 96h.

Media compositions have been described previously3,5,48 and are shown here. TSCM: DMEM/F12 with Glutamax (Gibco) supplemented with 0.2% v/v FBS (Gibco), 0.3% wt/vol BSA (WAKO), 1% ITS-X (Gibco), 2.5gml1 l-ascorbic acid-2-phosphate (Sigma), 50ngml1 EGF (Peprotech AF-100-15), 6M CHIR99021 (Tocris 4423), 0.5M A83-01 (Tocris 2939), 1M SB43154 (Tocris 1614), 0.8mM VPA (Sigma, dissolved in H2O) and 5M Y-27632 (Millipore 688000). TOM: Advanced DMEM/F12, N2 supplement (at manufacturers recommended concentration), B27 supplement minus vitamin A (at manufacturers recommended concentration), Primocin 100gml1, N-Acetyl-l-cysteine 1.25mM, l-glutamine 2mM, recombinant human EGF 50ngml1, CHIR99021 1.5M, recombinant human R-spondin-1 80ngml1, recombinant human FGF-2 100ngml1, recombinant human HGF 50ngml1, A83-01 500nM, prostaglandin E2 2.5M, Y-27632 5M. EVTM1: Advanced DMEM/F12 (or DMEM/F12 for TSC-EVTM 2D), l-glutamine 2mM, 2-mercaptoethanol 0.1mM, penicillin/streptomycin solution 0.5% (vol/vol), BSA 0.3% (wt/vol, WAKO), ITS-X supplement 1% (vol/vol), NRG1 (Cell Signaling 5218SC) 100ngml1, A83-01 7.5M, knockout serum replacement 4% (vol/vol). EVTM2, Advanced DMEM/F12 (or DMEM/F12 for TSC-EVTM 2D), l-glutamine 2mM, 2-mercaptoethanol 0.1mM, penicillin/streptomycin solution 0.5% (vol/vol), BSA 0.3% (wt/vol, WAKO), ITS-X supplement 1% (vol/vol), A83-01 7.5M, Knockout serum replacement 4% (vol/vol) (this is the same as EVTM1 without NRG1). This medium can be stored at 4C for up to 1 week. EVTM3, DMEM/F12 (for TSC-EVTM 2D), l-glutamine 2mM, 2-mercaptoethanol 0.1mM, penicillin/streptomycin solution 0.5% (vol/vol), BSA 0.3% (wt/vol, WAKO), ITS-X supplement 1% (vol/vol), A83-01 7.5M (this is the same as EVTM1 without NRG1 or knockout serum replacement). This can be stored at 4C for up to 1 week.

Fresh frozen sections were removed from 80C storage and air dried before being fixed in 10% neutral buffered formalin for 5min. After rinsing with deionised water, slides were stained in Mayers haematoxylin solution for 90s. Slides were completely rinsed in 45 washes of deionised water, which also served to blue the haematoxylin. Aqueous eosin (1%) was manually applied onto sections with a pipette and rinsed with deionised water after 13s. Slides were dehydrated through an ethanol series (70%, 70%, 100%, 100%) and cleared twice in 100% xylene. Slides were coverslipped and allowed to air dry before being imaged on a Hamamatsu Nanozoomer 2.0HT digital slide scanner.

Large tissue section staining and fluorescent imaging were conducted largely as described previously49. Sections were cut from fresh frozen samples embedded in OCT at a thickness of 1016m using a cryostat, placed onto SuperFrost Plus slides (VWR) and stored at 80C until stained. Tissue sections were processed using a Leica BOND RX to automate staining with the RNAscope Multiplex Fluorescent Reagent Kit v2 Assay (Advanced Cell Diagnostics, Bio-Techne), according to the manufacturers instructions. Probes are listed in Supplementary Table 8. Prior to staining, fresh frozen sections were post-fixed in 4% paraformaldehyde in PBS for 68h, then dehydrated through a series of 50%, 70%, 100%, and 100% ethanol, for 5min each. Following manual pre-treatment, automated processing included heat-induced epitope retrieval at 95C for 15min in buffer ER2 and digestion with Protease III for 15min prior to probe hybridisation. Tyramide signal amplification with Opal 520, Opal 570, and Opal 650 (Akoya Biosciences) and TSA-biotin (TSA Plus Biotin Kit, Perkin Elmer) and streptavidin-conjugated Atto 425 (Sigma Aldrich) was used to develop RNAscope probe channels.

Stained sections were imaged with a Perkin Elmer Opera Phenix Plus High-Content Screening System, in confocal mode with 2m z-step size, using a 40 (NA 1.1, 0.149m/pixel) water-immersion objective. Channels: DAPI (excitation 375nm, emission 435480nm), Atto 425 (excitation 425nm, emission 463501nm), Opal 520 (excitation 488nm, emission 500550nm), Opal 570 (excitation 561nm, emission 570630nm), Opal 650 (excitation 640nm, emission 650760nm).

Confocal image stacks were stitched as two-dimensional maximum intensity projections using proprietary Acapella scripts provided by Perkin Elmer.

For the scRNA-seq experiments, cells were loaded according to the manufacturers protocol for the Chromium Single Cell 3 Kit v3.0, v3.1 and 5 v1.0 (10X Genomics). Library preparation was carried out according to the manufacturers protocol to attain between 2,000 and 10,000 cells per reaction. Libraries were sequenced, aiming at a minimum coverage of 20,000 raw reads per cell, on the Illumina HiSeq 4000 or Novaseq 6000 systems using the following sequencing format: (A) read 1: 26 cycles; i7 index: 8 cycles, i5 index: 0 cycles; read 2: 98 cycles; (B) read 1: 28 cycles; i7 index: 8 cycles, i5 index: 0 cycles; read 2: 91 cycles; (C) read 1: 28 cycles; i7 index: 10 cycles; i5 index: 10 cycles; read 2: 90 cycles (v3.1 dual).

For the multimodal snRNA-seq and scATAC-seq experiments, cells were loaded according to the manufacturers protocol for the Chromium Single Cell Multiome ATAC + Gene Expression v1.0 to attain between 2,000 and 10,000 cells per well. Library preparation was carried out according to the manufacturers protocol. Libraries for scATAC-seq were sequenced on Illumina NovaSeq 6000, aiming at a minimum coverage of 10,000 fragments per cell, with the following sequencing format; read 1: 50 cycles; i7 index: 8 cycles, i5 index: 16 cycles; read 2: 50 cycles.

Ten-micrometre cryosections were cut and placed on Visium slides, then processed according to the manufacturers instructions. In brief, sections were fixed with cold methanol, H&E stained and imaged on a Hamamatsu NanoZoomer S60 before permeabilization, reverse transcription and cDNA synthesis using a template-switching protocol. Second-strand cDNA was liberated from the slide and single-indexed libraries were prepared using a 10x Genomics PCR-based protocol. Libraries were sequenced (1 per lane on a HiSeq 4000), aiming for 300M raw reads per sample, with the following sequencing format; read 1: 28 cycles, i7 index: 8 cycles, i5 index: 0 cycles and read 2: 91 cycles.

For each sequenced single-cell and single-nucleus RNA-seq library, we performed read alignment to the 10X Genomics GRCh38 3.0.0 human reference genome, mRNA version for scRNA-seq samples and pre-mRNA version for snRNA-seq samples, latter created following instructions from 10X Genomics: https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/advanced/references#premrna. Quantification and initial quality control were performed using the Cell Ranger Software (version 3.0.2; 10X Genomics) using default parameters. Cell Ranger filtered count matrices were used for downstream analysis.

For each sequenced snRNA-seq and ATACseq (multiome) library, we performed read alignment to custom made genome consisting of 10X Genomics GRCh38 3.0.0 pre-mRNA human reference genome and 10X Genomics Cell Ranger-Arc 1.0.1 ATAC genome, created following instructions from 10X Genomics: https://support.10xgenomics.com/single-cell-multiome-atac-gex/software/pipelines/latest/advanced/references. Quantification and initial quality control were performed using the Cell Ranger-Arc Software (version 1.0.1; 10X Genomics) using default parameters. Cell Ranger-Arc filtered count matrices were used for downstream analysis.

We used Scrublet for cell doublet calling on a per-library basis. We used a two-step diffusion doublet identification followed by Bonferroni FDR correction and a significance threshold of 0.01, as described in50. Predicted doublets were not excluded from the initial analysis, but used afterwards to flag clusters with high doublet scores.

Souporcell51 was used to deconvolute (1) maternal and fetal origin of cells and nuclei in our scRNA-seq and snRNA-seq samples (including multiome snRNA-seq); (2) assignment of cells to individuals in pooled samples (namely, samples Pla_HDBR8768477, Pla_HDBR8715512 and Pla_HDBR8715514); and (3) organoids from multiple individuals. In some samples deconvolution into maternal or fetal origin by genotype was not possible which is probably owing to the highly skewed ratio of genotypes (either extremely high (>0.95) or extremely low (<0.05) ratio of maternal to fetal droplets). In those cases, maternalfetal origin of the cells was identified using known markers from ref. 13.

Souporcell (version 2.4.0) was installed as per instructions in https://github.com/wheaton5/souporcell and used in the following way:

path_to/singularity exec ./souporcell.sif souporcell_pipeline.py -i ./cellranger_path/possorted_genome_bam.bam -b ./cellranger_path/filtered_feature_bc_matrix/barcodes.tsv -f ./genome_path/genome.fa -t 8 -o souporcell_result -k 2 --skip_remap True --common_variants ./filtered_2p_1kgenomes_GRCh38.vcf

Where k=2 corresponds to the number of individuals to be deconvoluted (in our case either mother and fetus or pooled individuals H7 and H9 in samples Pla_HDBR8768477, Pla_HDBR8715512 and Pla_HDBR8715514. The accuracy of deconvolution was evaluated in downstream analysis once cluster identity was clear from either gene expression or predictions of logistic regression. In samples where deconvolution worked successfully, inter-individual doublets were further excluded from downstream analysis.

To assess which genes in the scRNA-seq and snRNA-seq data were high in ambient RNA (soup) signal (further referred to as noisy genes), the following approach was undertaken separately for all the scRNA-seq and snRNA-seq samples: (1) Read in all the raw and filtered count matrices for each sample produced by Cell Ranger Software. (2) Discard droplets with < 5 unique moleular identifiers (UMIs) (likely to be fake droplets from sequencing errors). (3) Only keep data from samples which we further consider as noisy (where Fraction reads in cells reported by Cell Ranger is less than 70% (guided by 10X Genomics recommendations: https://assets.ctfassets.net/an68im79xiti/163qWiQBTVi2YLbskJphQX/e90bb82151b1cdab6d7e9b6c845e6130/CG000329_TechnicalNote_InterpretingCellRangerWebSummaryFiles_RevA.pdf). (4) Take the droplets that are in raw but are not in filtered matrices considering them as empty droplets. (5) Concatenate all raw objects with empty droplets into 1 joint raw object and do the same for filtered. (6) For all genes calculate soup probability as defined with the following equation: (P={E}_{g}^{{rm{empty}},{rm{droplets}}}/({E}_{g}^{{rm{empty}},{rm{droplets}}}+{E}_{g}^{{rm{cells}}/{rm{nuclei}}})), where ({E}_{g}^{{rm{empty}};{rm{droplets}}}) is the total sum of expression (number of UMI counts) of gene g in empty droplets, and ({E}_{g}^{{rm{cells}}/{rm{nuclei}}}) is the total sum of expression counts of gene g in droplets that are considered as cells/nuclei by Cell Ranger. (7) For all genes calculate number of cells/nuclei where the gene is detected at >0 expression level (UMI counts). (8) Label genes as noisy if their soup probability exceeds 50% quantile of soup probability distribution - done separately for cells and for nuclei.

This approach was used to estimate noisy genes in (1) donor P13 samples and (2) all donors samples. Donor P13 noisy genes were excluded during mapping onto space (Visium, see Location of cell types in Visium data), whereas all donors noisy genes (labelled using nuclei-only derived threshold in step 8 to not over-filter genes based on the higher quality portion of the data which in our case in scRNA-seq) were excluded during all donors analysis of the whole atlas of all the cell states at the maternalfetal interface.

We integrated the filtered count matrices from Cell Ranger and analysed them with scanpy (version 1.7.1), with the pipeline following their recommended standard practises. In brief, we excluded genes expressed by less than three cells, excluded cells expressing fewer than 200 genes, and cells with more than 20% mitochondrial content. After converting the expression space to log(CPM/100 + 1), the object was transposed to gene space to identify cell cycling genes in a data-driven manner, as described in50,52. After performing principal component analysis (PCA), neighbour identification and Louvain clustering, the members of the gene cluster including known cycling genes (CDK1, MKI67, CCNB2 and PCNA) were flagged as the data-derived cell cycling genes, and discarded in each downstream analysis where applicable.

Next, to have an estimate of the optimal number of latent variables to be used later in the single-cell variational inference (scVI) workflow for dimensionality reduction and batch correction, we identified highly variable genes, scaled the data and calculated PCA to observe the variance ratio plot and decide on an elbow point which defined values of n_latent parameter which were then used to correct for batch effect by 10X library batch (sample) with scVI. Number of layers in scVI models was tuned manually to allow for better integration. The resulting latent representation of the data was used for calculating neighbourhood graph, UMAP and further Louvain clustering. For trophoblast organoid scRNA-seq and snRNA-seq, data were integrated with Harmony by donor using theta = 0 parameter.

Analysis was done separately for (a) donor P13 trophoblast compartment and (b) all donors data (all cell states). In both analyses (a) and (b) trophoblast data was analysed separately with consecutive rounds of re-analysis upon exclusion of clusters of noisy nature (exhibiting gene expression characteristic of more than 1 distinct population). In addition, in all donors analysis fibroblast (maternal and fetal separately) and maternal NK, T, myeloid, epithelial, endothelial and perivascular compartments were reanalysed separately using the approach described in the previous paragraph to achieve fine grain annotation.

Differential gene expression analysis was performed with limma (limma version 3.46.0, edgeR version 3.32.1) with cell_or_nucleus covariate (scRNA-seq or snRNA-seq (including multiome snRNA-seq) origin of each droplet) backwards along the trajectory that was derived using stOrder approach, namely for the following 6 comparisons: VCT-CCC vs VCT (VCT and VCT-p cell states together); EVT-1 vs VCT-CCC; EVT-2 vs EVT-1; iEVT vs EVT-2; GC vs iEVT; eEVT vs EVT-2. Only significant DEGs were considered for downstream analysis, namely those with FDR (bonferroni) < 0.05).

We processed scATAC-seq libraries coming from multiome samples (read filtering, alignment, barcode counting, and cell calling) with 10X Genomics Cell Ranger-Arc (version 1.0.1) using the pre-built 10X GRCh38 genome (version corresponding to Cellranger-arc 1.0.1) as reference. We called the peaks using an in-house implementation of the approach described in Cusanovich et al. 53 (available at https://github.com/cellgeni/cellatac, revision 21-099). In short, the genome was broken into 5-kb windows and then each cell barcode was scored for insertions in each window, generating a binary matrix of windows by cells. Matrices from all samples were concatenated into a unified matrix, which was filtered to retain only the top 200,000 most commonly used windows per sample. Using Signac (https://satijalab.org/signac/ version 0.2.5), the binary matrix was normalized with term frequency-inverse document frequency (TF-IDF) followed by a dimensionality reduction step using Singular Value Decomposition (SVD). The first latent semantic indexing (LSI) component was ignored as it usually correlates with sequencing depth (technical variation) rather than a biological variation53. The 230 top remaining components were used to perform graph-based Louvain clustering. Next, peaks were called separately on each cluster using macs254. Finally, peaks from all clusters were merged into a master peak set (that is, peaks overlapping in at least one base pair were aggregated) and used to generate a binary peak-by-cell matrix, indicating any reads occurring in each peak for each cell.

This analysis was done separately for (1) all multiome data at first and (2) trophoblast-only subset of the multiome data. In the latter analysis we used annotation labels from the RNA counterpart of the multiome samples to perform peak calling.

For each 10X Genomics Visium sample, we used Space Ranger Software Suite (version 1.1.0) to align to the GRCh38 human reference pre-mRNA genome (official Cell Ranger reference, version 3.0.0) and quantify gene counts. Spots were automatically aligned to the paired H&E images by Space Ranger software. All spots under tissue detected by Space Ranger were included in downstream analysis.

To locate the cell states in the Visium transcriptomics slides, we used the cell2location tool v0.06-alpha55. As reference, we used snRNA-seq data of donor P13. We used general cell state annotations from the joint all donors analysis (corresponding to donor P13 data), with the exception of the trophoblast lineage. Trophoblast annotations were taken from donor P13-only analysis of the trophoblast compartment. Using information about which genes are noisy (high in ambient RNA signal) in donor P13 snRNA-seq data (details in Filtering genes high in ambient RNA signal), we excluded those from the reference and Visium objects prior to cell2location model training which significantly improved the results of mapping (namely, eliminated off-target mapping of cell statesthat is, made results of mapping more specific to the correct anatomical regions). Following the tutorial at https://cell2location.readthedocs.io/en/latest/notebooks/cell2location_tutorial.html#Cell2location:-spatial-mapping, we trained cell2location model with default parameters using 10X library as a batch covariate in the step of estimation of reference cell-type signatures. Results were visualized with scanpy (version 1.7.1). Plots represent estimated abundance of cell types (cell densities) in Visium spots.

We used SpatialDE256 tissue segmentation algorithm to assign Visium spots to three anatomical regions: (1) placenta; (2) decidua and villi tips; and (3) myometrium. We used mRNA abundances from the deconvolution results obtained with cell2location17 in SpatialDE2 tissue segmentation. Assignment of obtained Visium spot clusters to regions was done upon visual inspection. Locations of certain fibroblast cell states indicative of the specific anatomical region (uterine smooth muscle cells, uSMC and dS cell states) were also used to guide this assignment. In addition, low-quality spots were discarded on the basis of not being under tissue and having low count and gene coverage (visual inspection).

For more details, please refer to the following notebook: https://github.com/ventolab/MFI/blob/main/2_inv_troph_trajectory_and_TFs/2-1_stOrder_inv_troph/S1_regions_analysis_for_SpCov_model_and_later_for_CellPhone.ipynb

To obtain a set of high-quality peaks for downstream analysis, we filtered out peaks that (1) were included in the ENCODE blacklist, (2) have a width outside the 2101,500bp range, and (3) were accessible in less than 5% of cells from a cellatac cluster. Low-quality cells were also removed by setting to 4 the minimum threshold for log1p-transformed total counts per cell.

We adopted the cisTopic approach57,58 for the core of our downstream analysis. cisTopic employs latent Dirichlet allocation (LDA) to estimate the probability of a region belonging to a regulatory topic (regiontopic distribution) and the contribution of a topic within each cell (topiccell distribution). The topiccell matrix was used for constructing the neighbourhood graph, computing UMAP projections and clustering with the Louvain algorithm. After this was done for all cell states, clusters corresponding to trophoblast cell states (based on the unbiased clustering done here and annotation labels coming from the RNA counterpart of this multiome data) were further subsetted and reanalysed following the same pipeline.

Next, we generated a denoised accessibility matrix (predictive distribution) by multiplying the topiccell and regiontopic distribution and used it to calculate gene activity scores. To be able to integrate them with scRNA-seq and snRNA-seq data, gene activity scores were rounded and multiplied by a factor of 107, as described58.

Final labels of invading trophoblast in snATAC-seq data were directly transferred from RNA counterpart of the multiome data.

StOrder is a computational framework for joint inference of cellular differentiation trajectories from gene expression data and information about location of cell states in physical space (further referred to as spatial data).

It consists of three principal steps:

Calculate pairwise cell state connectivity from gene expression data (here we use snRNA-seq data).

Calculate pairwise cell state proximity in physical space from spatial data (here we use Visium spatial transcriptomics data) using a new spatial covariance model.

Combine connectivity matrices from steps 1 and 2 in a weighted expression to reconstruct the putative tree structure of the differentiation trajectory.

First, StOrder relies on a gene expression-based connectivity matrix (generated in our case by PAGA59) that establishes potential connections between cell state clusters defined by single-cell or single-nucleus transcriptomics datasets. The values in this matrix can be interpreted as pairwise similarity scores for cell states in gene expression space. In our case we used snRNA-seq data from P13 as it contains all trophoblast subsets.

Second, StOrder generates a spatial covariance matrix that reflects pairwise proximity of cell states that co-exist in space and smoothly transition from one state to another while physically migrating in space. To do so, StOrder takes as an input the deconvolution results (derived in our case with cell2location17) of Visium spatial transcriptomics data. Here, we used all spatial transcriptomics data profiled (donors P13, P14 and Hrv43). Then, it fits a Gaussian process model that derives pairwise spatial covariance scores for all the cell state pairs with the following model:

$${rm{vec}}({{bf{Y}}}_{i},{{bf{Y}}}_{j}) sim {mathscr{N}},left(0,,left(begin{array}{cc}{sigma }_{1}^{(1)} & {sigma }_{2}^{(1)}\ {sigma }_{2}^{(1)} & {sigma }_{3}^{(1)}end{array}right)otimes K({bf{X}},l)+left(begin{array}{cc}{sigma }_{1}^{(2)} & 0\ 0 & {sigma }_{2}^{(2)}end{array}right)otimes {bf{I}}right)$$

where is the Kronecker product and the combined vector of cell densities (Yi,k, Yj,k) of cell states i and j is modelled by a multivariate Gaussian distribution whose covariance decomposes into a spatial and a noise term. The spatial term

$$left(begin{array}{cc}{sigma }_{1}^{(1)} & {sigma }_{2}^{(1)}\ {sigma }_{2}^{(1)} & {sigma }_{3}^{(1)}end{array}right)otimes Kleft({bf{X}},lright)$$

is defined by a between-cell-state covariance matrix

$$left(begin{array}{cc}{sigma }_{1}^{(1)} & {sigma }_{2}^{(1)}\ {sigma }_{2}^{(1)} & {sigma }_{3}^{(1)}end{array}right)$$

and a spatial covariance matrix defined using the squared exponential kernel:

$$K{({bf{X}},l)}_{mn}=exp left(-frac{{parallel {x}_{m}-{x}_{n}parallel }^{2}}{2{l}^{2}}right)$$

xm and xn are spatial coordinates of spots m and n and l is the length scale of the smooth Gaussian process function in space that is being fit to cell densities.

The noise term

$$left(begin{array}{cc}{sigma }_{1}^{(2)} & 0\ 0 & {sigma }_{2}^{(2)}end{array}right)otimes {bf{I}}$$

represents sources of variation other than spatial covariance of cell state densities.

The between-cell-state covariance matrix is constrained to be symmetric positive definite by defining

$$left(begin{array}{cc}{sigma }_{1}^{(1)} & {sigma }_{2}^{(1)}\ {sigma }_{2}^{(1)} & {sigma }_{3}^{(1)}end{array}right)=,left(begin{array}{cc}{a}_{1} & 0\ {a}_{2} & {a}_{3}end{array}right){left(begin{array}{cc}{a}_{1} & 0\ {a}_{2} & {a}_{3}end{array}right)}^{{rm{T}}}$$

The free parameters a1, a2, a3, 1(2), 2(2) and l are estimated using maximum likelihood and automatic differentiation in Tensorflow60,61 using the BFGS algorithm. To improve convergence, we initialize l to the distance between centres of neighboring Visium spots.

This model allows us to infer which cell states are proximal in physical space and are likely to be migrating in the process of gradual differentiation in space.

For the spatial covariance model within StOrder workflow we only used a subset of our Visium data that corresponded to (1) decidua_and_villi_tips and (2) myometriumbecause only these regions contained invading trophoblast cell states. For more details please see Subsetting Visium data into anatomical regions with SpatialDE2 in Downstream analysis of 10x Genomics Visium data above. This helps to focus on the regions of the tissue that are relevant for the process of interest and is recommended to do in general if there are parts of the Visium data that do not contain cell states relevant to the process of interest.

Third, StOrder reconstructs connections between cell states by taking into account both the connectivity matrix (step 1) from single-cell transcriptomics data and the spatial covariance matrix (step 2) from the spatial data in the following way:

$${beta }({alpha }P+(1-{alpha })S)+(1-{beta })Podot S$$

where P is the PAGA connectivity matrix, S is the spatial correlation matrix, weights the contributions of P and S in the additive term, weights the contributions of the additive and multiplicative terms, and is the element-wise product. It then reconstructs the putative trajectory tree using the built-in PAGA functions.

The combined connectivity matrix based on both gene expression and spatial data with a range of weight parameters revealed the fully resolved invasion trajectory tree of the EVT with the correct topology (all connected cell state components, one branching point, no cycles, start at VCT-CCC population and two end points: eEVT and GC populations). The choice of parameter (contribution/weight of gene expression vs spatial part in the final matrix) in this last step depends on the goal of using this approach. In our case, we assumed: (1) the origin of EVT (VCT-CCC); (2) the end points of EVT (eEVT and GC); (3) the determination of a single branching point; and (4) the absence of cyclic trajectory. We therefore produced trajectory trees for 10,201 of (,) value pairs (from 0 to 1 with 0.01 increment step each) representative of different tree topologies corresponding to different ratios of gene expression vs spatial contribution. Out of the 10,201 tree structures we inspected, 3,574 trees represented the topology with the assumptions described above. These trajectories consistently assigned EVT-2 as the putative branching point. Tree structures with mainly gene expression-based connectivity values did not yield a branching point population we were looking for. Tree structures with mainly spatial based connectivities hindered the link between iEVT and GC populations, likely due to the large length scale of this invasion in space.

Our approach assumes the gradual nature of gene expression changes accompanied by gradual migration of cells in space while they differentiate. Thus, it may not yield meaningful results in scenarios where this underlying assumption is violated. In addition, it is recommended that the user estimates the spatial scale at which the process of interest is taking placewhether in current Visium resolution the differentiation and migration is happening over the course of only a few spots or many morethis will change the initial values of l parameter and help the model fit the data better.

Gene expression (snRNA-seq) counts of the multiome data for donor P13 were normalized by total counts (scanpy.pp.normalize_per_cell(rna, counts_per_cell_after=1e4)) and log-transformed (pp.log1p(rna)). Highly variable gene features were then calculated (sc.pp.highly_variable_genes(rna, min_mean=0.0125, max_mean=3, min_disp=0.5)) and the subsetted objects expression was scaled (sc.pp.scale(rna, max_value=10)).

Chromatin accessibility (scATAC-seq) counts of the multiome data for donor P13 were preprocessed using TF-IDF normalization (muon.atac.pp.tfidf(atac[key], scale_factor=1e4)). To select biologically meaningful highly variable peak features, ATAC counts were aggregated into pseodubulks by cell states and averaged, then variance of accessibility was calculated across these pseudobulks, and informative peak features were selected based on this measure (top 75th percentile (10,640) of peaks selected in total) as the peaks with highest variance. Finally, these data were scaled (sc.pp.scale(atac, max_value=10)).

Using the preprocessed RNA and ATAC data we used a pseudotime-aware dimensionality reduction method MEFISTO30 to extract major sources of variation from the RNA and ATAC data jointly and identify coordinated patterns along the invasion trajectory. As a proxy for the trophoblast invasion trajectory in the MEFISTO model we used 2-dimensional pseudotime coordinates based on a UMAP of the RNA data by calculating PCA (sc.tl.pca(rna, n_comps=8)), neighborhood graph (sc.pp.neighbors(rna)) and UMAP embedding (sc.tl.umap(rna)).

The MEFISTO model was trained using the following command within MUON (version 0.1.2) package interface:

muon.tl.mofa(mdata, outfile=,

use_obs = union,

smooth_covariate=[UMAP1, UMAP2],

use_float32=True)

We further excluded factor 5 from downstream analysis as a technical artefact due to its significant and high correlation (Spearman rank-order correlation coefficient 0.94 (over all cell states), P<10308, two-sided test) with the n_peaks_by_counts (number of ATAC peaks with at least 1 count in a nucleus) in ATAC view in all cell states (Supplementary Fig. 4k) and lack of smoothness along pseudotime (Supplementary Fig. 4j).

To further interpret ATAC features, we annotated them based on their genomic location using GenomicRanges package (version 1.42.0). In parallel, we used epigenetic data from62 to mark peak features in close proximity to trophoblast-specific enhancer features. To do so, we used peak files corresponding to H3K4me1, H3K27ac and H3K27me3 histone modifications marks for second trimester trophoblast samples (obtained from authors of aforementioned study upon request) to infer regions of the genome corresponding to active (H3K27ac + H3K27me3), primed (only H3K4me1) or repressed (H3K4me1 + H3K27me3) enhancers. This was done using bedtools (version 2.30.0) in the following way:

bedtools subtract -a H3K4me1_file.bed -b H3K27ac_file.bed > interm_file.bed bedtools subtract -a interm_file.bed -b H3K27me3_file.bed > primed_enhancers.bed To produce primed enhancers file

bedtools intersect -a H3K4me1_file.bed -b H3K27ac_file.bed > active_enhancers.bed To produce active enhancers file

bedtools intersect -a H3K4me1_file.bed -b H3K27me3_file.bed > repressed_enhancers.bed To produce repressed enhancers file

The enhancer files produced were then overlapped with peaks in ATAC analysis (bedtools intersect -a atac_peaks_file.bed -b enhancer_file.bed -wa) and any peaks having a >1-bp overlap with an enhancer feature were considered to be proximal to those features (done separately for active, primed and repressed enhancers).

Gene set enrichment analysis for gene features was performed based on the C5 category and the Biological Process subcategory from the MSigDB database (https://www.gsea-msigdb.org/gsea/msigdb) using GSEA functionality implemented in MOFA2 (run_enrichment command, MOFA2 version 1.3.5). This was done separately for negative and positive weights of each factor.

Peak group enrichment for peak features was performed using the same run_enrichment command in MOFA2 on peak groups defined as described above (Defining groups of ATAC peak features).

To extract information about transcription factor binding motif enrichment in ATAC features of MEFISTO factors, we first performed enrichment analysis of peaks using GSEA functionality implemented in MOFA2 (run_enrichment command, MOFA2 version 1.3.5) on the peak-motif matrix produced by Signac package (version 1.5.0). Then, to identify which MEFISTO factors contribute the most to each transition of cell states along the invading trophoblast trajectory (inferred with StOrder), we trained logistic regression classifiers for each transition along the trajectory (overall for 6 transitions: VCTVCT-CCC, VCT-CCCEVT-1, EVT-1EVT-2, EVT-2iEVT, iEVTGC, EVT-2eEVT) on the matrix of factor values. For each transition the factor with the highest absolute coefficient separating the two cell states was selected, accounting for the sign of contribution in the logistic regression (positive or negative). If the top factor is contributing to a transition with a positive coefficient, transcription factor binding motifs coming from MEFISTO enrichment analysis of this factors top positive values are further considered in general transcription factor analysis as transcription factors upregulated upon this transition, whereas transcription factor binding motifs coming from MEFISTO enrichment analysis of this factors top negative values are further considered in general transcription factor analysis as transcription factors downregulated upon this transition. All of these transcription factor motifs are marked as having evidence from the MEFISTO factor relevant for this transition. Reverse procedure is applied in case if the top factor is contributing to a transition with a negative coefficient in the corresponding logistic regression model.

For more details please see the following notebook: https://github.com/ventolab/MFI/blob/main/2_inv_troph_trajectory_and_TFs/2-5_MEFISTO_analysis_inv_troph/S3_DEG_comparison_to_MEFISTO_factor_translation.ipynb

To derive trophoblast pseudotime based on transcriptomic similarity, we used Slingshot v1.8.0. With Slingshot we fitted a cluster-based minimum spanning tree (MST) over the two-dimensional UMAP of P13 trophoblasts, and inferred the global lineage topology to assign cell states to lineages. Only donor P13 cells in the G1 phase of the cell cycle were included. To balance trophoblast state contributions, we downsampled each trophoblast state to account for up to 100 cells per state. VCT was assigned as the initial cell state (start.clus), while eEVT, SCT and GC were assigned as terminal states (end.clus). Slingshot fits simultaneous principle curves to smooth the MST and assigns a weight for each trophoblast cell in each lineage. Slingshot outputs lineage-specific pseudotimes and weights of assignment for each cell.

We next fitted a tradeSeq (v1.4.0) gene expression model (negative binomial generalized additive model) using the trajectory pseudotime and the weights computed with Slingshot (with nknots=6). Next, we tested whether the gene expression is significantly changing along trophoblast pseudotime. For such a purpose, we used the statistical test implemented in the associationTest function, which tests the null hypothesis that all smoother coefficients are equal to each other. Genes with a P<106 and mean logFC>0.5 were selected as the main drivers of the trophoblast trajectory.

To transfer cell labels from donor P13 snRNA-seq in vivo trophoblast to the scRNA-seq TSC and PTO we trained two independent logistic regression models. The P13 dataset was downsampled to 500 cells per trophoblast state, except for GC and eEVT, which were discarded from the training due to their scarcely abundance. The common highly variable genes (1,695 genes for PTO and 1,565 for TSC), of the 4,000 selected per dataset, between the in vivo and each individual organoid dataset were selected as features for model training. The in vivo dataset was split into 80/20 training and test set, hyperparameters were explored employing a threefold cross-validation and scored employing the mean Matthews correlation coefficient of each fold. Top-ranked models were selected and assessed on the test set, with no significant differences found between them. Finally, the best model for each organoid dataset was employed to transfer cell labels from donor P13.

To retrieve interactions between invading trophoblast and other cell populations identified in our samples, we used the CellPhoneDB degs_analysis method13,63 (https://github.com/ventolab/CellphoneDB) described in ref. 33. In short, we retrieved the interacting pairs of ligands and receptors meeting the following requirements: (1) all the protein members were expressed in at least 10% of the cell type under consideration; and (2) at least one of the protein members in the ligand or the receptor was a DEG in an invading trophoblast subset (according to our analysis of differential expression, for details please see Differential gene expression analysis), with an adjusted P-value below 0.05 and logFC>0.1. We further selected which cell states are spatially co-located in each microenvironment via visual inspection of cell2location deconvolution results for our Visium data. The analysis was done on an updated version of CellPhoneDB-database (v4.1) which includes novel intercellular interactions from refs. 64,65. Only bona fide manually curated interactions were considered in the analysis.

To prioritize the transcription factors relevant for each invading trophoblast cell state or microenvironment, we integrate four types of measurements: (1) expression levels of the transcription factor and (2) the activity status of the transcription factor measured from (2a) the expression levels of their targets (described in Transcription factor activities derived from scRNA-seq and snRNA-seq) and/or (2b) the chromatin accessibility of their binding motifs (described in Transcription factor motif activity analysis from scATACseq) and/or (2c) evidence of the chromatin accessibility of their binding motifs in relevant factors from multimodal RNA-ATAC analysis (with MEFISTO). Plots in main figures include transcription factor meeting the following criteria: (1) transcription factor was differentially expressed, with adjusted P-value<0.05) and/or (2) transcription factor was differentially active, with log2 fold change greater than 0.25 and adjusted P-value<0.05 in at least one of the transcription factor activity measurements (2a or 2b).

We compute differential expression using the procedure described in Differential gene expression analysis and further subset resulting gene targets to transcription factors only based on the list of transcription factors provided by DoRothEA.

We estimated protein-level activity for human transcription factor as a proxy of the combined expression levels of their targets. Target genes were retrieved from Dorothea66, an orthogonal collection of transcription factor targets compiled from a range of different sources. Next, we estimated transcription factor activities for each cell using Viper67, a GSEA-like approach, as implemented in the Dorothea R package and tutorial68 for the genes differentially expressed along the invading trophoblast trajectory (see Differential gene expression analysis).

Transcription factor motif activities were computed using chromVar69 v. 1.12.2 with positional weight matrices from JASPAR201870, HOCOMOCOv1071, SwissRegulon72, HOMER73. chromVar returns a matrix with binding activity estimates of each transcription factor in each cell, which we used to test for differential transcription factor binding activity between trophoblast cell states with FindMarkers function in Seurat (default parameters) in the same way as described in Differential gene expression analysis (backwards along invading trophoblast trajectory).

Further information on research design is available in theNature Portfolio Reporting Summary linked to this article.

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Spatial multiomics map of trophoblast development in early pregnancy - Nature.com

Introducing Dr Freya Storer – new Research Associate in Developmental Biology – Imperial College London

We interviewed Dr Freya Storer - a new Research Associate in Developmental Biology who joined us in February.

Can you tell me a little about your background?

I was born into a family of contrasting interests. My father studied engineering and now works in intellectual property, whilst my mother studied languages, going on to teach French and Italian. Being surrounded by different passions has always helped me to explore topics using a holistic approach.

Although I am a British national, I had a largely European upbringing. Due to my fathers job, we moved around, after a brief period in the UK, we moved to Normandy (France) and later to Bavaria (Germany). It was only as I left for university that I established myself back in the UK, and even then, it hasnt been static. The freedom to move and explore education through different cultures has been a privilege and lends itself to the collaborative nature of academia.

Can you tell us a bit about your study prior to now?

My interest in biosciences was nurtured at the University of Bristol, where I read Cellular and Molecular Medicine, a course that married genetic mechanisms with microbiology and disease. It established a broad foundation on techniques to study cellular processes, applying them to diverse medical contexts from cancer metastasis to pathogen-host evasion tactics. It was during my final year project that I was first introduced to the organism that I work with today.

Drosophila melanogaster, a well-characterised invertebrate model, has been central to many discoveries in the field of genetics and comes with an array of technical possibilities. In my bachelors, I worked with Prof. Will Wood and Dr Andrew Davidson on a project investigating the key regulators of actin during debris clearance by Drosophila embryonic haemocytes. Thanks to this opportunity, I pursued a research masters in a neighbouring lab, under Dr Marc Amoyel, where I studied how differing translation initiation mechanisms might drive the decision of stem cell fate in the Drosophila testis niche.

Having committed myself to the fruit fly, I ventured into the field of neuroscience during my PhD at the Dementia Research Institute in Cardiff. With the support of Dr Gaynor Smith, I identified two genes that may regulate the immune responses of the brain contributing to Alzheimers pathology. Most recently, I worked with Dr Peter Lawrence and Dr Jose Casal at Cambridge University, where we studied the distribution of Frizzled during pupal development to understand the mechanisms that govern planar cell polarity.

What is your new role at Imperial?

I recently joined Dr Tony Southalls team as the newest Research Associate. Here, I am working on a unique project to understand the genetic factors that mediate neuronal cell fate plasticity. We have come far since Waddingtons landscape model of cell differentiation, with increasing evidence suggesting that cells may in fact (under certain conditions) return to an earlier state. We hope to use novel tools, such as adapted CRISPR methods and DamID to explore how the shift between eu- and hetero-chromatin states, through most translational modifications, can restrict plasticity. Research is a core part of my position, but I am also devoted to cultivating a friendly and inspiring learning environment for aspiring researchers and students.

What motivated you to work in this area?

My project incorporates all my experience and interests, combining my extensive knowledge of cellular mechanisms and development, with Drosophila biology. It means it feels familiar, but I am also learning something new in epigenetics, a complex research field which I have always been captured by and wanted to study. I am also honoured to work with Dr Tony Southall, who has already made tremendous progress in the field and designed numerous innovative tools to study it. I believe it is easy to be motivated to study biosciences, as it satisfies a curiosity for the natural world with the possibility of contributing to new therapies that may solve devastating diseases.

What attracted you to working in Life Sciences at Imperial?

I was excited to be a part of the rich history of discovery that has produced so many internationally renowned and impactful scientists, from Nobel laureates to pioneering female scientists such as Helen Porter and Winifred Watkins. The project I am working on feels meaningful and I feel privileged to share the halls with some of todays brilliant minds, who inspire me to go beyond my expectations of research. I am arriving at an exciting time in Imperials evolution, with the introduction of the White City Campus expanding the possibilities for entrepreneurship and multidisciplinary research. Beyond that, London is home to a large community of geneticists and entomologists, providing a space for collaboration and the sharing of ideas.

Tell us an interesting/unusual fact about yourself.

Other than my obvious enthusiasm for science and research, I love music. I am an avid listener but have also enjoyed performing in bands as a singer/keyboardist.

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Introducing Dr Freya Storer - new Research Associate in Developmental Biology - Imperial College London

First-of-its-kind stem cell study sheds light on Klinefelter syndrome – Medical Xpress

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The impact of X overdosage on the global transcriptomes of Saudi and ENA KS-iPSCs. A) Venn diagram showing the DEGs shared in the contrast 47,XXY Vs. 46,XY in iPSCs generated from ENA and Saudi KS patients. B) Gene Ontology analysis on common DEGs using the GO enriched for Biological Processes (BP), Molecular Functions (MF), and Cellular components (CC). C) KEGG enrichment analysis and D) MGI Mammalian phenotype disease pathway analysis on Saudi and ENA common DEGs. Credit: Endocrine Connections (2023). DOI: 10.1530/EC-22-0515

In a research partnership between King Abdullah University of Science and Technology (KAUST), King Abdulaziz University, Jeddah (KAU), and King Abdulaziz University Hospital, Jeddah (KAUH), scientists have conducted a first-of-its-kind study in the Kingdom that compares stem cells derived from a unique cohort of Saudi Klinefelter patients with a group of North American and European descent.

Klinefelter is a chromosomal disease characterized by an extra chromosome X in the cells of males. Frequent clinical features of the syndrome are infertility, intellectual disability, metabolic syndrome and type 2 diabetes among others, and one out of every six hundred Saudi males are affected.

However, the MENA population is largely underrepresented when it comes to studying the impact of the genomic background on disease susceptibility and prognosis. The majority of studies involving the use of iPSCs have been performed using North American and European patients.

The KAUST-KAU-HAUH study addresses this gap using a "patient-derived induced pluripotent stem cells" (iPSC)-based disease modeling study aimed at understanding the molecular basis of Klinefelter syndrome. By using skin, blood, hair or urine-derived cell samples with the iPSC approach, it is possible to bring the patient's cells back to the embryonic state in which they developed, and use them to model the onset and progression of diseases "in a dish."

"The Kingdom is benefitting from the world-class collaboration between our three leading research entities," said Vice President for Research Pierre Magistretti, Distinguished Professor and director of the KAUST Smart-Health Initiative. "The iPSC technology is revolutionizing the study of the molecular mechanisms of diseases as it provides a way to work on human cells derived from patients."

Magistretti added that the platform for iPSC that KAUST scientists have developed allow for unique collaborations with clinical centers such as KAU KAUH and with the support of KAUST Smart-Health Initiative.

The results from this first joint Saudi study demonstrate the existence of a subset of genes residing on the X chromosome, whose dysregulation specifically characterizes Klinefelter syndrome, regardless of the geographical area of origin, ethnicity and genetic makeup.

"This Saudi iPSC cohort will serve as an ideal cellular platform to explore further work into chromosomal diseases," said Antonio Adamo, assistant professor and principal investigator in the Stem Cell and Diseases Laboratory at KAUST. "For example, it would be particularly interesting modeling neurodevelopment and anatomical changes affecting gray and white matters, features typically observed in Klinefelter Syndrome."

This cellular platform will be used to generate the so-called "mini-brains," three-dimensional cultures of patient-derived cells resembling the human brain that can be used to study the molecular mechanisms underlying the neurodevelopmental features of the disease. The findings yield an in vitro model suitable for the development of personalized medicine applications.

The findings are published in the journal Endocrine Connections.

More information: Veronica Astro et al, A transcriptomic signature of X chromosome overdosage in Saudi Klinefelter syndrome induced pluripotent stem cells, Endocrine Connections (2023). DOI: 10.1530/EC-22-0515

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First-of-its-kind stem cell study sheds light on Klinefelter syndrome - Medical Xpress

What!! Mice born to two biological fathers in Japan: Report – Hindustan Times

Japanese scientists have created a mice with two biological fathers, according to a report from The Guardian. This breakthrough could have significant implications for same-sex couples looking to have biological children. Additionally, the technique may also be useful in treating severe forms of infertility, including Turner's syndrome, a condition where one copy of the X chromosome is missing or partially missing, which was the primary motivation of the research, according to the scientists.

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This is the first case of making robust mammal oocytes from male cells, said Katsuhiko Hayashi, who led the work at Kyushu University in Japan.

While scientists have previously used complex genetic engineering techniques to create mice with two biological fathers, a significant headway has now been achieved. For the first time, viable eggs have been successfully cultivated from male cells, making the process less complicated and more accessible.

Also Read | Babies' gut microbiome is not affected by vaginal microbiome: Study

In order to produce viable eggs from male cells, the study required a series of complex procedures. The first step involved reprogramming male skin cells into a state similar to that of stem cells, known as induced pluripotent stem (iPS) cells.

The Y chromosome of these cells was then removed, and an X chromosome from another cell was inserted, resulting in iPS cells that possessed two identical X chromosomes. This technique allowed the researchers to create viable eggs with an XX chromosome combination, despite starting with male XY cells.

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The trick of this, the biggest trick, is the duplication of the X chromosome, said Hayashi. We really tried to establish a system to duplicate the X chromosome.

After undergoing the complex process of transforming male skin cells into viable eggs, the cells were grown in a specialized culture system called an ovary organoid. This system was designed to mimic the conditions present in a mouse ovary. When the eggs were fertilized with normal sperm, the researchers were able to obtain approximately 600 embryos, which were then implanted into surrogate mice.

This resulted in the successful birth of seven mouse pups. However, the efficiency of the process was found to be lower than that achieved using normal female-derived eggs, with only around 1% of the embryos resulting in a live birth compared to around 5% with traditional eggs.

The study noted that human cells require longer periods of cultivation to produce a mature egg, which can increase the risk of acquiring unwanted genetic changes. The translation of this technique to human cells would require a substantial leap in research, especially considering that scientists are still working to create lab-grown human eggs from female cells.

Professor Amander Clark of the University of California, Los Angeles, who works on lab-grown gametes, said that translating the work into human cells would be a "huge leap" because scientists have yet to create lab-grown human eggs from female cells.

Scientists have created human egg precursors, but the cells have stopped developing before meiosis, a critical step in cell division required for the development of mature eggs and sperm.Were poised at this bottleneck at the moment, Clark said

She stressed that the next steps are an engineering challenge and getting through that could be 10 years or 20 years.

Trainee Content Producer at Hindustan Times Digital Stream. India's regional languages attract me.

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What!! Mice born to two biological fathers in Japan: Report - Hindustan Times

What a startups woolly mammoth meatball tells us about the future of meat – The Indian Express

While this will be a one-off creation, perhaps garner publicity for the food-tech company, founder Tim Noakesmith told the AP that through the mammoth meatball, the company hoped to start a conversation around global meat consumption.

Cultivated meat also called cultured or cell-based meat is made from animal cells but livestock does not need to be killed in order to produce it. Notably, it is different from plant-based meat substitutes in that it actually uses animal DNA to recreate in a lab the taste and texture of meat. Plant-based substitutes, on the other hand, try and mimic the taste and texture of meat using other plant-based alternatives.

For the woolly mammoth project, Vow worked with Prof Ernst Wolvetang, at the University of Queenslands Australian Institute for Bioengineering, The Guardian reported. The aim was to create the mammoth muscle protein from available DNA. Prof Wolvetangs team took the DNA sequence for mammoth myoglobin, a key muscle protein in giving the meat its flavour, and filled in any gaps using the DNA of the African elephant, the closest living relative of the extinct woolly mammoth.

The prepared DNA sequence was then placed in myoblast (embryonic precursor to muscle cells) stem cells from a sheep, which soon replicated in the right lab conditions to grow to the nearly 20 billion cells subsequently used by the company to create the mammoth meatball.

It was ridiculously easy and fast, Prof Wolvetang told The Guardian. We did this in a couple of weeks. Initially, the idea was to produce dodo meat. However, the DNA sequences required for that do not exist.

The mammoth meatball has not been tasted by anyone, even its creators. Nor does Vow plan to put it into commercial production. Instead, the idea has been to use the meatball to start a much-needed conversation.

We wanted to get people excited about the future of food being different to potentially what we had before. That there are things that are unique and better than the meats that were necessarily eating now, and we thought the mammoth would be a conversation starter and get people excited about this new future, Noakesmith told the AP.

But also the woolly mammoth has been traditionally a symbol of loss. We know now that it died from climate change. And so what we wanted to do was see if we could create something that was a symbol of a more exciting future thats not only better for us, but also better for the planet, he added.

Multiple studies have pointed out the massive impact that the global meat industry has on the environment. According to the Food and Agriculture Organization of the United Nations (FAO), global meat consumption has increased significantly in recent decades, with per capita consumption almost doubling since the early 1960s.

This means that roughly 14.5 per cent of global emissions of greenhouse gases are attributable to livestock farming. This includes not just carbon dioxide but also methane and nitrous oxide, which scientists say have a climate warming potential of anywhere between 25 times and 300 times higher than that of carbon dioxide.

Most greenhouse gas emissions from plant-based foods are lower than those linked to animal-based foods.

Experts say that if cultivated/cultured meat is widely adopted, it could vastly reduce the environmental impact of global meat production in the future.

By cultivating beef, pork, chicken, and seafood, we can have the most impact in terms of reducing emissions from conventional animal agriculture and satisfying growing global demand for meat while meeting our climate targets, Seren Kell, science and technology manager at Good Food Institute, a nonprofit that promotes plant- and cell-based alternatives to animal products, told the AP.

This is because cultivated meat uses much less land and water than livestock, and produces no methane emissions. The industry can run on energy produced purely from renewable sources. While the woolly mammoth meatball is, as was planned, an unconventional idea, most of the industry has been focussing on commonly consumed meats like pork, chicken and beef.

However, there is a long way to go before cultivated meat becomes mainstream across the world. Currently, Singapore is the only country to have approved cell-based meat for consumption. Vow hopes to enter the market later this year, with its quail-based meat product.

More than getting regulatory approvals, for cultivated meat to really take off, there has to be a massive, global-scale behavioural and cultural change. We have a behaviour change problem when it comes to meat consumption, George Peppou, CEO of Vow, told The Guardian.

Projects such as Vows woolly mammoth meatball help draw attention and start conversations on the possibilities of cultivated meat. (This) will open up new conversations about cultivated meats extraordinary potential to produce more sustainable foods, Kell told the media.

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What a startups woolly mammoth meatball tells us about the future of meat - The Indian Express

‘The Biggest Challenge Is To Find Out How 98% of DNA Regulates … – Kashmir Life

A young geneticist, Dr Rais A Gania was surprised to see his takeaways from his PhD were part of the text well before he entered the classroom as a teacher. Credited for identifying a particular enzyme that helps in crucial DNA copying, he is serving the IUSTs molecular medicine centre. In a freewheeling interview, he opens up about his research and future plans

KASHMIR LIFE (KL): You studied in Kashmir and worked in different universities all over the world. How was your learning curve and what were the challenges you faced?

DR RAIS A GANAI (DRAG): I was born and brought up in the Posh-Kirri village of Anantnag. I did my primary schooling at Government Primary School in the same village. Later, I went to the Government Middle in the nearby Hugam village. Later, my father suggested me to complete further studies in Srinagar, as he was working at the University of Kashmir. Then, I went to the Starland High School Zakura and completed my matriculation there.

In Srinagar, I found it very difficult to compete with students because of the language barrier, as the medium of instruction was different. It was a challenging task to learn English and Urdu languages. It took me a lot of time to cope with the level of the students.

Then I completed my 10+2 from Soura Higher Secondary School. Afterwards, I went to the Islamia College of Science and Commerce, where from I completed my graduation. Even though there was not an ample structure at that time but the laboratories were well established. Attendance of labs was mandatory, due to which my scientific temper got developed.

After that, I was selected at the University of Kashmir for a couple of courses but I chose to study Biotechnology. After completing the Masters degree in Biotechnology, I went to the Indian Institute of Science (IISc), Bangalore, where I worked under the mentorship of Prof Umesh Varshney and worked intensely on various Biotechnological challenges. He invested a lot of money, time and effort and taught me many new things due to which my interest in the research further deepened. During this time a few of my research papers were published.

Then I went to Sweden in 2009 for my PhD and completed it in 2015 and later got an international Postdoc fellowship offer in Sweden amounting to Rs 2.5 crore. I used that fellowship and immigrated to the USA. There I joined the NewYork based Howard Huges Medical Institute. I did research there for almost 2-3 years under the well-known researcher Danny Reinberg.

Then I came back to Kashmir as a Ramanujan Fellow. Initially, I joined the Central University of Kashmir and later moved to the IUSTs Watson-Crick Centre for Molecular Medicine in 2020.

KL: The work on genetics has been going on in all major universities throughout the world. However, we still have not understood the gene fully. What are the various challenges in understanding the gene, and what are the different goalposts we still have to reach?

DRAG: The gene is actually a small DNA sequence made of sugar bases like Adenine, Guanine, Thymine, and Cytosine (A, G, T, C). They are about 3 billion sugar bases called Nucleotides (made of Deoxyribose sugar, the Phosphate group, and the Nitrogen base) in a DNA molecule arranged in a chain structure. All the Nucleotides in a DNA molecule do not constitute genes, but only 1-2 per cent makes the genes and the rest 98-99 per cent of the base pairs do not attribute to the genes.

Scientists have identified most of the genes in our body and their functioning but the functioning of the rest 98 per cent of the non-genomic sequences (regulatory sequences) is still not known. We only know that these contain non-genomic sequences that regulate the genes, but the biggest challenge is to find out how 98 per cent of DNA regulates the 1 per cent of DNA.

The other major challenge was to understand the three-dimensional structure of DNA and its arrangement inside the cell. The chromosomes are arranged in compartmental structures. How and when these compartments are formed is yet to be discovered. How these genes are activated and repressed in the cells is still a challenge.

The actual structure of a DNA molecule has a three-dimensional chromatin architecture. These DNA molecules are present on the chromosomes. Our body contains 46 chromosomes in each cell that are intertwined inside the cell. The intertwined structure of chromosomes helps in the better expression of genes during cell division and cell formation. All the required genes express together and activate simultaneously in order to form a complete cell.

KL: What was your PhD all about and what were the major takeaways from your research?

DRAG: As I mentioned that DNA is a small molecule contained in a cell. A cell contains two meters of intertwined DNA, which if stretched is equivalent to at least four times the distance between the sun and the earth. During cell division and cell multiplication, this DNA is replicated/ duplicated which has to be very accurate. Genetic defects during cell division cause mutations/errors, which lead to genetic diseases, metabolic disorders, or even cancer.

During my PhD, my research was about the role of an enzyme called DNA polymerase in DNA replication. This enzyme reads, copies, and then makes the exact copy of a parent DNA molecule. The three billion nucleotides of a DNA molecule in a cell are copied accurately without any error or defect with the help of this enzyme. Besides, it also rectifies the errors, which are caused during cell division and helps in errorless duplication. Thus, the DNA polymerase enzyme not only plays a role in DNA replication but also fixes the errors caused during DNA replication, if any.

I also studied the functioning of various other enzymes but the pivotal research was about DNA polymerase. The majority of DNA polymerase enzymes look like, if I can say, a right-hand structure, containing a thumb, a palm, and fingers. The DNA polymerase, we studied has an additional domain called the P-domain, unlike the other DNA-Polymerase enzymes which only have three domains. The majority of DNA-Polymerase enzymes require a scaffold or support (called PCNA) for DNA copy and replication, but the DNA-polymerase we studied does not require PCNA rather it has the inbuilt P-domain that helps in DNA synthesis and thus does not require an outside scaffold. This was the biggest takeaway from my PhD research.

To my surprise, I later found when I was at the Central University of Kashmir, that our work and findings were published in textbooks, and are being taught to students in different Universities all over the world. It was a very difficult project to work on because nobody prior to us had worked on this. Our work was then published in the Journal Nature Structural and Molecular Biology, which now is a part of the textbooks and is being taught.

KL: What was your Post-doctorate research about, and what were your accomplishments and learnings during that period?

DRAG: I mostly studied two things during my Postdoc research, the role of epigenetic factors in the development, and the development of stem cells into the cardiomyocyte.

I actually wanted to expand and diversify my expertise, so I shifted to the field of epigenetics.

Under epigenetics, we study how the genes present in the DNA are regulated. Let us understand it this way if we have two monozygotic twins and one of them is raised by the adopted parents and the other by the natural parents. Technically, after 30 years of age, both should be identical because of the principle of monozygotic nature, but because of the environmental effects, they would have developed variations over time. It is because the influence of environmental conditions affects the development of an individual and that regulates the body. Thus, the effect of an environment on the development over time, beyond the genetic basis and beyond DNA is called epigenetics.

There are thousands of genes on a DNA molecule and there are specific factors that actually regulate the functioning of these genes. I also worked on these factors.

DNA is wrapped around by the histone proteins. These proteins contain chemical modifications or tags that determine the function of the DNA sequence. I worked on early embryonic development, particularly on stem cells. I studied how differentiated development takes place from a single cell into different kinds of complex organs i.e., how a stem cell is transformed into a cardiomyocyte.

KL: How could you make lawmen understand this differentiation of a stem cell into different complex organs? What really controls this differentiation of cells? Is this also part of epigenetics?

DRAG: Nobody really knows how embryonic development occurs as it is not easy to study this field. People have now started research on it.

During embryonic development, the fusion of egg and sperm results in the formation of a Zygote, which later undergoes the 2-cell stage and the 4-cell stage, and so on. From day one of development certain genes are activated which stimulates the Zygote division and this division activates other genes, which then cause muscle cell formation. More and more genes get activated that guide the muscle cells to transform into different complex organs. It is mostly like this, but there is still ambiguity on how embryonic development takes place through different stages of development.

KL: What is your role at the IUSTs Watson-Crick Centre for Molecular Medicine and what are the different domains you are working on?

DRAG: I am establishing my lab here for research purposes. Besides, I am also the coordinator of the B Voc course on the Medical Lab and Molecular Diagnostic Technology. I teach students also. I guide students on how to do diagnostic tests and the process of opening diagnostic clinics.

The primary part of my job at the Watson-Crick Centre is to do research along with my students who work with me on the continuation of my PhD research work. We are studying the role of DNA polymerase enzyme other than the role of DNA synthesis.

Secondarily, we are also studying epigenetics. Epigenetic marks at different positions of a DNA molecule, other than the normal positions cause diseases like cancer, and developmental and neurodegenerative diseases, among others. Therefore, our aim is to research epigenetics in detail in order to develop drugs for the treatment of these diseases.

Mujtaba Hussain processed the interview

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'The Biggest Challenge Is To Find Out How 98% of DNA Regulates ... - Kashmir Life