Cell-type-directed design of synthetic enhancers.

Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes1. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Here we show that deep learning models2-6, can be used to efficiently design synthetic, cell-type-specific enhancers, starting from random sequences, and that this optimization process allows detailed tracing of enhancer features at single-nucleotide resolution. We evaluate the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We further exploit enhancer design to create 'dual-code' enhancers that target two cell types and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the state space searches towards local optima, we characterize enhancer codes through the strength, combination and arrangement of transcription factor activator and transcription factor repressor motifs. Finally, we apply the same strategies to successfully design human enhancers, which adhere to enhancer rules similar to those of Drosophila enhancers. Enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.

Small chromosomal regions position themselves autonomously according to their chromatin class.

The spatial arrangement of chromatin is linked to the regulation of nuclear processes. One striking aspect of nuclear organization is the spatial segregation of heterochromatic and euchromatic domains. The mechanisms of this chromatin segregation are still poorly understood. In this work, we investigated the link between the primary genomic sequence and chromatin domains. We analyzed the spatial intranuclear arrangement of a human artificial chromosome (HAC) in a xenospecific mouse background in comparison to an orthologous region of native mouse chromosome. The two orthologous regions include segments that can be assigned to three major chromatin classes according to their gene abundance and repeat repertoire: (1) gene-rich and SINE-rich euchromatin; (2) gene-poor and LINE/LTR-rich heterochromatin; and (3) gene-depleted and satellite DNA-containing constitutive heterochromatin. We show, using fluorescence in situ hybridization (FISH) and 4C-seq technologies, that chromatin segments ranging from 0.6 to 3 Mb cluster with segments of the same chromatin class. As a consequence, the chromatin segments acquire corresponding positions in the nucleus irrespective of their chromosomal context, thereby strongly suggesting that this is their autonomous property. Interactions with the nuclear lamina, although largely retained in the HAC, reveal less autonomy. Taken together, our results suggest that building of a functional nucleus is largely a self-organizing process based on mutual recognition of chromosome segments belonging to the major chromatin classes.

Concurrent whole-genome haplotyping and copy-number profiling of single cells.

Methods for haplotyping and DNA copy-number typing of single cells are paramount for studying genomic heterogeneity and enabling genetic diagnosis. Before analyzing the DNA of a single cell by microarray or next-generation sequencing, a whole-genome amplification (WGA) process is required, but it substantially distorts the frequency and composition of the cell's alleles. As a consequence, haplotyping methods suffer from error-prone discrete SNP genotypes (AA, AB, BB) and DNA copy-number profiling remains difficult because true DNA copy-number aberrations have to be discriminated from WGA artifacts. Here, we developed a single-cell genome analysis method that reconstructs genome-wide haplotype architectures as well as the copy-number and segregational origin of those haplotypes by employing phased parental genotypes and deciphering WGA-distorted SNP B-allele fractions via a process we coin haplarithmisis. We demonstrate that the method can be applied as a generic method for preimplantation genetic diagnosis on single cells biopsied from human embryos, enabling diagnosis of disease alleles genome wide as well as numerical and structural chromosomal anomalies. Moreover, meiotic segregation errors can be distinguished from mitotic ones.

Urine of Preterm Neonates as a Novel Source of Kidney Progenitor Cells.

In humans, nephrogenesis is completed prenatally, with nephrons formed until 34 weeks of gestational age. We hypothesized that urine of preterm neonates born before the completion of nephrogenesis is a noninvasive source of highly potent stem/progenitor cells. To test this hypothesis, we collected freshly voided urine at day 1 after birth from neonates born at 31-36 weeks of gestational age and characterized isolated cells using a single-cell RT-PCR strategy for gene expression analysis and flow cytometry and immunofluorescence for protein expression analysis. Neonatal stem/progenitor cells expressed markers of nephron progenitors but also, stromal progenitors, with many single cells coexpressing these markers. Furthermore, these cells presented mesenchymal stem cell features and protected cocultured tubule cells from cisplatin-induced apoptosis. Podocytes differentiated from the neonatal stem/progenitor cells showed upregulation of podocyte-specific genes and proteins, albumin endocytosis, and calcium influx via podocyte-specific transient receptor potential cation channel, subfamily C, member 6. Differentiated proximal tubule cells showed upregulation of specific genes and significantly elevated p-glycoprotein activity. We conclude that urine of preterm neonates is a novel noninvasive source of kidney progenitors that are capable of differentiation into mature kidney cells and have high potential for regenerative kidney repair.

Hydrop enables droplet-based single-cell ATAC-seq and single-cell RNA-seq using dissolvable hydrogel beads.

Single-cell RNA-seq and single-cell assay for transposase-accessible chromatin (ATAC-seq) technologies are used extensively to create cell type atlases for a wide range of organisms, tissues, and disease processes. To increase the scale of these atlases, lower the cost and pave the way for more specialized multiome assays, custom droplet microfluidics may provide solutions complementary to commercial setups. We developed HyDrop, a flexible and open-source droplet microfluidic platform encompassing three protocols. The first protocol involves creating dissolvable hydrogel beads with custom oligos that can be released in the droplets. In the second protocol, we demonstrate the use of these beads for HyDrop-ATAC, a low-cost noncommercial scATAC-seq protocol in droplets. After validating HyDrop-ATAC, we applied it to flash-frozen mouse cortex and generated 7996 high-quality single-cell chromatin accessibility profiles in a single run. In the third protocol, we adapt both the reaction chemistry and the capture sequence of the barcoded hydrogel bead to capture mRNA, and demonstrate a significant improvement in throughput and sensitivity compared to previous open-source droplet-based scRNA-seq assays (Drop-seq and inDrop). Similarly, we applied HyDrop-RNA to flash-frozen mouse cortex and generated 9508 single-cell transcriptomes closely matching reference single-cell gene expression data. Finally, we leveraged HyDrop-RNA's high capture rate to analyze a small population of fluorescence-activated cell sorted neurons from the Drosophila brain, confirming the protocol's applicability to low input samples and small cells. HyDrop is currently capable of generating single-cell data in high throughput and at a reduced cost compared to commercial methods, and we envision that HyDrop can be further developed to be compatible with novel (multi) omics protocols.

Scientists are now able to determine the order of chemical blocks, or nucleic acids, that make up the genetic code. These sequencing tools can be used to identify which genes are active within a biological sample. They do this by extracting and analysing open chromatin (regions of DNA that are accessible to the cell's machinery), or sequences of RNA (the molecular templates cells use to translate genes into working proteins). Initially, most sequencing tools could only provide an 'averaged-out' profile of the genes activated in bulk pieces of tissue which contain multiple types of cell. However, advances in technology have led to new methods that can extract and analyse open chromatin or RNA from individual cells. First, the cells are separated, via a technique called microfluidics, into tiny droplets of water along with a single bead that carries a unique barcode. The cell is then broken apart inside the droplet and the barcode within the bead gets released and attaches itself to the genetic material extracted from the cell. All the genetic material inside the droplets is then pooled together and sequenced. Researchers then use the barcode tags to identify which bits of RNA or DNA belong to each cell. Single-cell sequencing has many advantages, including being able to pinpoint precise genetic differences between healthy and abnormal cells, and to create cell atlases of whole organisms, tissues and microbial communities. But existing methods for extracting chromatin are very expensive, and there were no openly available tools for processing thousands of cells at speed. Furthermore, while several single-cell RNA sequencing tools are already freely available, they are not very sensitive or practical to use. Here, De Rop et al. have developed a new open-source platform called HyDrop that overcomes these barriers. The method entails a new type of barcoded bead and optimised elements of existing microfluidics protocols using open-source reagents. These changes created a more user-friendly workflow and increased sensitivity of sequencing at no additional cost. De Rop et al. used their new platform to screen the RNA and open chromatin of thousands of individuals cells from the brains of mice and flies. HyDrop outperformed other open-source methods when working in RNA-sequencing mode. It also provides the first open-source tool for sequencing open chromatin in single cells. Further improvements are expected as researchers tweak the platform, which for now provides an affordable alternative to existing methods.

Urine-Derived Kidney Progenitor Cells in Cystinosis.

Nephropathic cystinosis is an inherited lysosomal storage disorder caused by pathogenic variants in the cystinosin (CTNS) gene and is characterized by the excessive shedding of proximal tubular epithelial cells (PTECs) and podocytes into urine, development of the renal Fanconi syndrome and end-stage kidney disease (ESKD). We hypothesized that in compensation for epithelial cell losses, cystinosis kidneys undertake a regenerative effort, and searched for the presence of kidney progenitor cells (KPCs) in the urine of cystinosis patients. Urine was cultured in a specific progenitor medium to isolate undifferentiated cells. Of these, clones were characterized by qPCR, subjected to a differentiation protocol to PTECs and podocytes and assessed by qPCR, Western blot, immunostainings and functional assays. Cystinosis patients voided high numbers of undifferentiated cells in urine, of which various clonal cell lines showed a high capacity for self-renewal and expressed kidney progenitor markers, which therefore were assigned as cystinosis urine-derived KPCs (Cys-uKPCs). Cys-uKPC clones showed the capacity to differentiate between functional PTECs and/or podocytes. Gene addition with wild-type CTNS using lentiviral vector technology resulted in significant reductions in cystine levels. We conclude that KPCs present in the urine of cystinosis patients can be isolated, differentiated and complemented with CTNS in vitro, serving as a novel tool for disease modeling.

Activation of Skeletal Stem and Progenitor Cells for Bone Regeneration Is Driven by PDGFRß Signaling.

Bone repair and regeneration critically depend on the activation and recruitment of osteogenesis-competent skeletal stem and progenitor cells (SSPCs). Yet, the origin and triggering cues for SSPC propagation and migration remain largely elusive. Through bulk and single-cell transcriptome profiling of fetal osterix (Osx)-expressing cells, followed by lineage mapping, cell tracing, and conditional mouse mutagenesis, we here identified PDGF-PDGFRß signaling as critical functional mediator of SSPC expansion, migration, and angiotropism during bone repair. Our data show that cells marked by a history of Osx expression, including those arising in fetal or early postnatal periods, represent or include SSPCs capable of delivering all the necessary differentiated progeny to repair acute skeletal injuries later in life, provided that they express functional PDGFRß. Mechanistically, MMP-9 and VCAM-1 appear to be involved downstream of PDGF-PDGFRß. Our results reveal considerable cellular dynamism in the skeletal system and show that activation and recruitment of SSPCs for bone repair require functional PDGFRß signaling.

Mouse model of chromosome mosaicism reveals lineage-specific depletion of aneuploid cells and normal developmental potential.

Most human pre-implantation embryos are mosaics of euploid and aneuploid cells. To determine the fate of aneuploid cells and the developmental potential of mosaic embryos, here we generate a mouse model of chromosome mosaicism. By treating embryos with a spindle assembly checkpoint inhibitor during the four- to eight-cell division, we efficiently generate aneuploid cells, resulting in embryo death during peri-implantation development. Live-embryo imaging and single-cell tracking in chimeric embryos, containing aneuploid and euploid cells, reveal that the fate of aneuploid cells depends on lineage: aneuploid cells in the fetal lineage are eliminated by apoptosis, whereas those in the placental lineage show severe proliferative defects. Overall, the proportion of aneuploid cells is progressively depleted from the blastocyst stage onwards. Finally, we show that mosaic embryos have full developmental potential, provided they contain sufficient euploid cells, a finding of significance for the assessment of embryo vitality in the clinic.

Tracing the origin of disseminated tumor cells in breast cancer using single-cell sequencing.

BACKGROUND: Single-cell micro-metastases of solid tumors often occur in the bone marrow. These disseminated tumor cells (DTCs) may resist therapy and lay dormant or progress to cause overt bone and visceral metastases. The molecular nature of DTCs remains elusive, as well as when and from where in the tumor they originate. Here, we apply single-cell sequencing to identify and trace the origin of DTCs in breast cancer. RESULTS: We sequence the genomes of 63 single cells isolated from six non-metastatic breast cancer patients. By comparing the cells' DNA copy number aberration (CNA) landscapes with those of the primary tumors and lymph node metastasis, we establish that 53% of the single cells morphologically classified as tumor cells are DTCs disseminating from the observed tumor. The remaining cells represent either non-aberrant "normal" cells or "aberrant cells of unknown origin" that have CNA landscapes discordant from the tumor. Further analyses suggest that the prevalence of aberrant cells of unknown origin is age-dependent and that at least a subset is hematopoietic in origin. Evolutionary reconstruction analysis of bulk tumor and DTC genomes enables ordering of CNA events in molecular pseudo-time and traced the origin of the DTCs to either the main tumor clone, primary tumor subclones, or subclones in an axillary lymph node metastasis. CONCLUSIONS: Single-cell sequencing of bone marrow epithelial-like cells, in parallel with intra-tumor genetic heterogeneity profiling from bulk DNA, is a powerful approach to identify and study DTCs, yielding insight into metastatic processes. A heterogeneous population of CNA-positive cells is present in the bone marrow of non-metastatic breast cancer patients, only part of which are derived from the observed tumor lineages.