A Nanoliter-Scale Nucleic Acid Processor with Parallel Architecture

Jong Wook Hong, Vincent Studer, Giao Hang, W French Andreson, Stephen R Quake

presented by: Anna Shcherbina

Michael Meyer

Goal: Use Single Cell To Establish cDNA Library

Gene Expression Profile

Motivation for Single-Cell mRNA/ DNA Extraction

Primary cells hard to obtain in large quantities .

Isolating cells from animals or patients results in a mixtureof cell types.

Epigenetic variation between cells withidentical genotypes influences development.

Existing Technologies and their Limitations

Affinity capture and elution of purified DNA from silicon microstructures

Deep Reactive Ion Etching (DRIE) On the order of uL, not nL No parallelization No integration

Microarrays measure expression of a few genes from a single cell

Amplification process introduces distortion Require choice of finite set of possible transcripts

Currently, cDNA library construction methods requires 1000-10,000 input cells.

Innovation: Microfluidic Chip to Sequentially Process nL Volumes and Isolate Cells

Small-Volume Scaling Process IntegrationFabricated by multi-layer soft lithography

Lysing and purification performed directly on the chip.

No pre/post-treatment needed.

Compatible with many biological assays

Protein crystallization nL -volume PCR FACS single-cell enzyme screening

Fig 1. a. Layout of microfluidic chip, version 1. Channels are 100 um wide. Fluidic ports are named; actuation ports are numbered 1-11.

Lysing buffer chamber

cell chamber

bead chamber

b. Photograph of the in situ affinity column construction. Scale bar 200 um. c. Cell loaded into cell chamber before lysis step. Scale bar 100 um. (Hong et al.)

mRNA Purification Chip Design

affinity

column

Performance & Sensitivity Assay

Fig 2. RT-PCR analysis of isolated mRNA. RT-PCR products analyzed on 2% agarose gel loaded with 5% of reaction. (Hong et al.)

Primers used to identify high abundance B-actin transcript and moderate abundance OZF transcript. 18 experiments performed.

5 of these used a single cell.

Chip Sensitivity Assay Results

B-actin purified from cells in 14 out of 18 experiments Between 2-10 cells required to detect OZF mRNA signal Monotonically increasing relationship between band intensity and cell number in B-actin mRNA. No functional relationship observed in non-normalized data.

Fig 3. RT-PCR products for both transcripts were analyzed on a 2% agarose gel, whose bands were quantified and normalized. Zero values indicate absence of detectable band in gel. (Hong et al.)

•Parallelization

•Align several linear processors and use same cross-junction structures to load them simultaneously.

•Loading & Processing Flow

•Loading--fluid flows north/south

•Processing--fluid flows east/west, along each batch processor

•Customization

DNA Purification Chip Architecture (advances)

Fig 4. Temporal action of DNA isolation circuitry (Hong et al.)

•Each processor hold 5 nL

•Volume of cells used: 1.6, 1.0, 0.4 nL

•remaining volume is reaction buffer and lysis buffer

Full Chip and Experimental Setup - Characterization of Sensitivity

Fig 5. Food-coloring reveals the interconnectivity of chip. (Hong et al.)

Fig 6. Verification of the successful recovery of E. coli genomic DNA. Samples have been PCR amplified. (Hong et al.)

a. Undiluted E. coli cultureLane 1: 1.6 nL culture (~1120 cells)Lane 2: 1.0 nL culture (~700 cells)Lane 3: 0.4 nL culture (~280 cells)Lanes 4-6: Negative control (pure H2O)

b. 1:10 dilutionsLanes 1,4,7: diluted 1.6 nLLanes 2,5,8: diluted 1.0 nLLanes 3,6,9: diltued 0.4 nL

c. Intensity of gel bands

DNA Yield Experimental Results

Potential Uses and Impact

•Increasing throughput in single-cell analysis

•Automation of reagent preparation for large cell populations •industrial-scale microarray analysis

•Preparation step for environmental analysis or medical diagnostics

•Tool for microculture and analysis of slow-growing or unculturable bacteria

•Generation of subtractive libraries from pairs of single cells •eliminate commonly expressed transcripts &•enrich differentially expressed transcripts