Recording development with single cell dynamic lineage tracing

Abstract

Every animal grows from a single fertilized egg into an intricate network of cell types and organ systems. This process is captured in a lineage tree: a diagram of every cell's ancestry back to the founding zygote. Biologists have long sought to trace this cell lineage tree in individual organisms and have developed a variety of technologies to map the progeny of specific cells. However, there are billions to trillions of cells in complex organisms, and conventional approaches can only map a limited number of clonal populations per experiment. A new generation of tools that use molecular recording methods integrated with single cell profiling technologies may provide a solution. Here, we summarize recent breakthroughs in these technologies, outline experimental and computational challenges, and discuss biological questions that can be addressed using single cell dynamic lineage tracing.

Keywords: Lineage tracing; Lineage trees; Single cell.

Fig. 1.

Lineage tracing in development. (A) The cell lineage tree describes the successive cell divisions that generate all of the organs and tissues within an organism. Lineage tracing aims to capture these cellular relationships. (B) Rare mutations (marked by colored dots) occur sporadically across the genome during development. These mutations can be used to trace complete lineage trees, but require whole- or partial-genome sequencing. (C) A classic labeling approach is to tag a subset of cells with a dye or fluorescent marker such as GFP (green); this approach is good for tagging all descendants of the marked cell but does not reveal relationships within the marked population. (D) Cellular barcoding, using viruses or transposons, can label a population of cells with unique identifiers (purple and green). Descendant cells can then be assigned to common progenitors. These approaches can capture clonal relationships but cannot infer relationships within each subpopulation. (E) Dynamic lineage-tracing approaches add increasing information over developmental time. These approaches can assign cells to progenitors and determine branches within the lineage tree.

Fig. 2.

DNA barcoding and recovery using prospective lineage recording. (A) Cellular barcoding introduces a diverse population of nucleotide tags into the genome, typically using viral integration or transposition. These static barcodes can be read by PCR and short-read sequencing. (B) Barcode diversity can also be generated with recombinases, whereby an array of DNA-binding sites can be scrambled when the recombinase is expressed. These barcodes are typically read with long-read sequencing due to their large size (see Peikon et al., 2014). (C) Self-targeting CRISPR guide RNAs target Cas9 editing to their own genomic sequence, resulting in repeated rounds of mutation that can generate barcode diversity. The resulting sequences can be read using short-read sequencing. (D) With MEMOIR, genetic regions termed ‘scratchpads’ are inserted into the genome and collapsed by CRISPR editing. The resulting scratchpads are expressed as mRNA and detected with fluorescent in situ hybridization. (E) Alternatively, multi-target barcodes – in one or more locations in the genome – can be edited with a single guide RNA or distinct guide RNAs to incrementally add information to the barcode during development. There barcodes are typically recovered using short-read sequencing.

Fig. 3.

The future of dynamic lineage tracing. Single cell transcriptomes and barcode recordings can be recovered from a variety of animals, tissues and organoids using sequencing and/or imaging approaches. These data can be organized into cell types using dimensionality reduction approaches. The cell lineage tree serves as an organizing scaffold to relate single cells and cell types with embedded recordings of spatial context, signaling landmarks and other measures of cellular state. The output will be richly annotated trees of development and disease.