Below are detailed technological achievements for each of the three objectives:
We have Developed a prototype efficient cell lineage discovery workflow.
The Duplex MIPs based cell lineage workflow is composed of (a) Design of duplex MIPs precursor: Desired targets are selected from our cell lineage database and precursors are designed; (b) Duplex MIPs preparation: duplex MIPs precursors are synthesized on microarray, collected and amplified by PCR as a pool. PCR product is digested to remove the universal adaptors (red and green); the digested product is purified and diluted to obtain active duplex MIPs; (c) Duplex MIPs and template DNA are mixed together, the targeting arms (blue and yellow) anneal to the flanking regions of the targets and the MIPs are then circularized by gap filling with DNA polymerase and ligase. Linear DNA, including excess MIPs and template DNA, is digested by exonucleases and an Illumina sequencing library is generated by adding adaptors and barcodes using PCR for each sample separately. Libraries are pooled and sequenced by Illumina NGS platform, followed by analysis of the raw reads to detect mutations. This mutation information is then used to infer the cell lineage tree (FIG. 1).
Our computational pipeline produce targeted single-cell (SC) sequencing data, and uses it to generate a lineage tree of the input cells. This pipeline consists of our error model, designed to address the noise that is caused in vitro by the polymorphic nature of STRs. The stutter error model is based on analyzing synthetic STR sequencing library to calibrate a Markov model for the prediction of stutter patterns in any amplification cycle. This biallelic STR signal is used as the input of genotyping algorithm, based on approximately –Maximum-likelihood approach to construct the lineage tree. Different statistical approaches, such as bootstrapping are also implemented along the way to evaluate the quality of the signal and validate the stability of the lineage tree.
An end-to-end automated system for the analysis of SC DNA, targeted for 14K MS loci has been implemented and deployed in the Weizmann servers farm (FIG. 5).
We presented eSTGt, a programming and simulation environment for population dynamics background. The language captures in broad terms the effect of the changing environment while abstracting away details on interaction among individuals. An eSTG program consists of a set of stochastic tree grammar transition rules that are context-free. When executing a program, the tool generates the corresponding lineage trees as well as the internal states values. The presented tool allows researchers to use existing biological knowledge in order to model the dynamics of a developmental process and analyze its behavior throughout the historical events. Simulated lineage trees can be used to validate various hypotheses in silico and to predict the behavior of dynamical systems under various conditions.
We Demonstrated the feasibility and value of human cell lineage discovery via collaborative proof-of-concept experiments.
Below is a list of few of our collaborators whose samples after processing and analysis yielded satisfactory results. In all the below figures, the colors represents different biological classifications that were classified by our collaborators, the width of the branches of the tree represents p-value which is a hypergeometric significance score, that was calculated for these different classifications, and the triangles represents the bootstrap that was calculated for the tree structure, representing its statistical stability.
• Christoph Klein: Experimental Medicine, University of Regensburg, Germany - Uncovering mechanisms of metastasis formation by lineage analysis of individual primary tumor and metastatic cells.
Fig.12: Lineage tree for malignant melanoma patient.
Fig.16: Lineage tree reconstruction for breast cancer tumor cells.
• Tsila Zuckerman, Hematology & Transplantation, Rambam, Haifa, Israel. Study Leukemia at diagnosis, remission and relapse, resistance to chemotherapy and relapse initiation.
FIG.19 20: Lineage tree reconstruction for Acute Myeloid Leukemia tumor cells.
• Ruth Halaban, Department of Dermatology, Yale U. New Haven, CT, USA , Department of Dermatology, Yale U. New Haven, CT, USA
FIG. 3: Cell lineage reconstruction of SCs from melanoma metastases and PBLs from the patient YUCLAT
• Ruby Shalom Feuerstein, Department of Genetics and Developmental Biology Rappaport Faculty of Medicine Technion - Israel Institute of Technology.
FIG. 21: Cell lineage tree for cornea cells
