Chemokinetic response of spermatozoa to progesterone gradients.

<p>(A-C) Representative trajectories of sperm in Group A, B and C (18 sperm in each plot). Each colored line is a cell trajectory that is 3 s long. Black dots are the endpoints of the trajectories. (D-F) Different chemokinetic parameters were compared among three groups. Group A, 100 pM progesterone solution was added in peripheral channels; Group B, 1 mM progesterone solution was added; Group C, control. Data are presented as mean ± SD (n = 5). **: <i>p</i> < 0.05.</p

Chemotactic responses of spermatozoa to progesterone gradients.

<p>(A) An overview of concentration gradients generated in the hexagon. Progesterone solution was added in every other channel of the chip (red). Concentration gradients were generated in three regions in the hexagon corresponding to peripheral channels where progesterone solution was loaded. Blue square showed the field where sperm chemotaxis were observed. (B) A microscopic photograph of spermatozoa with several trajectories indicated (18 sperm). Each colored line represented a sperm trajectory within 3 s. (C-E) Comparisons of chemotactic parameters among three groups. Group A, 100 pM progesterone solution was added in peripheral channels; Group B, 1 mM progesterone solution was added; Group C, control. Data are presented as mean ± SD (n = 5). **: <i>p</i> < 0.05.</p

Gradient formation in the microfluidic device.

<p>(A) Fluorescein solution was doped into one channel and concentration gradient was formed in the hexagon (marked as green). Red square showed the area where fluorescence signals were recorded. (B) A representative fluorescence intensity change at 15 min, 30 min, 1 h, 2 h, 4 h and 7 h were displayed in sequence from left to right, top to bottom. (C) Normalized fluorescence intensity profiles in the central hexagon.</p

Simulation analysis of fluid flow in the device.

<p>(A) Schematic illustration of the microfluidic device. A and B/C represent the inlets and outlets of the channels, respectively. (B) Representative simulation of pressure distribution in the device. (C) Representative simulation of flow velocity distribution in the device. Blue arrow indicates the direction of fluid flow. (D) Distribution curves of flow velocity along the chip. I: peripheral channel; II: interconnecting grooves; III: central hexagon. Regions that were analyzed were indicated in (C) by a black dashed line. P1, P2 and P3 refers to different loading plans listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0142555#pone.0142555.t001" target="_blank">Table 1</a>.</p

Design and fabrication of the microfluidic device.

<p>(A) Design of the microfluidic device. (B) A photograph of the chip. (C) A three-dimensional illustration of the device. Liquid level in Pool A was higher than that in both Pool B/C (details were listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0142555#pone.0142555.t001" target="_blank">Table 1</a>). (D) Fabrication process of the device (pictures were not drawn to scale).</p

Characterization of fluid flow using microspheres.

<p>(A) Illustration of characterization experiment. Channels doped with microspheres were marked as yellow. The microsphere solution was added both in the inlet and outlet of the this channel. Other channels as well as the central hexagon were doped with SWM (marked as white). Red square indicated the region where (C) was captured. (B) The speed of microspheres in different region of the device (I: peripheral channel; II: interconnecting grooves; III: central hexagon) at 0, 15 min, 30 min after sample loading. P1, P2 and P3 were corresponded to the loading plans in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0142555#pone.0142555.t001" target="_blank">Table 1</a>. Data are presented as Mean ± SD (n = 5). (C) The distribution of microspheres in the device when liquid level in each pool reached equilibrium in different loading plans (P1, P2 and P3, successively).</p

Initial volume of solution and hydrostatic pressure in each loading pool.

<p>^a. Hydrostatic pressure was calculated using the formula P = ρgh (ρ-the density of the solution, considered close to water; g-gravitational acceleration, 9.8 m/s²; h-liquid height in each pool). Liquid height in each pool was calculated using the formula V = <i>πr</i>²<i>h</i> (V refers to volume of solution added in each pool, diameter of each pool was 2 mm).</p><p>^b. ΔH/ΔP refers to liquid height/hydrostatic pressure differences between Pool A and B/C.</p><p>^c. Pool A, B and C were corresponded to the loading pools labeled in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0142555#pone.0142555.g001" target="_blank">Fig 1C</a>.</p><p>Initial volume of solution and hydrostatic pressure in each loading pool.</p

Simultaneous Generation of Gradients with Gradually Changed Slope in a Microfluidic Device for Quantifying Axon Response

Over the past decades, various microfluidic devices have been developed to investigate the role of the molecular gradient in axonal development; however, there are very few devices providing quantitative information about the response of axons to molecular gradients with different slopes. Here, we propose a novel laminar-based microfluidic device enabling simultaneous generation of multiple gradients with gradually changed slope on a single chip. This device, with two asymmetrically designed peripheral channels and opposite flow direction, could generate gradients with gradually changed slope in the center channel, enabling us to investigate simultaneously the response of axons to multiple slope gradients with the same batch of neurons. We quantitatively investigated the response of axon growth rate and growth direction to substrate-bound laminin gradients with different slopes using this single-layer chip. Furthermore, we compartmented this gradient generation chip and a cell culture chip by a porous membrane to investigate quantitatively the response of axon growth rate to the gradient of soluble factor netrin-1. The results suggested that contacting with a molecular gradient would effectively accelerate neurites growth and enhance axonal formation, and the axon guidance ratio obviously increased with the increase of gradient slope in a proper range. The capability of generating a molecular gradient with continuously variable slopes on a single chip would open up opportunities for obtaining quantitative information about the sensitivity of axons and other types of cells in response to gradients of various proteins