of 8
TECHNIQUES AND RESOURCES
RESEARCH REPORT
NaNuTrap: a technique for
in vivo
cell nucleus labelling
using nanobodies
Zsuzsa Ákos, Leslie Dunipace and Angelike Stathopoulos
*
ABSTRACT
In vivo
cell labelling is challenging in fast developmental processes
because many cell types differentiate more quickly than the
maturation time of fluorescent proteins, making visualization of
these tissues impossible with standard techniques. Here, we
present a nanobody-based method, Nanobody Nuclear Trap
(NaNuTrap), which works with the existing Gal4/UAS system in
Drosophila
and allows for early
in vivo
cell nuclei labelling
independently of the maturation time of the fluorescent protein. This
restores the utility of fluorescent proteins that have longer maturation
times, such as those used in two-photon imaging, for live imaging of
fast or very early developmental processes. We also present a more
general application of this
system, whereby NaNuTrap can convert
cytoplasmic GFP expressed in any existing transgenic fly line into a
nuclear label. This nuclear re-localization of the fluorescent signal can
improve the utility of the GFP label, e.g. in cell counting, as well as
resulting in a general increase in intensity of the live fluorescent
signal. We demonstrate these capabilities of NaNuTrap by effectively
tracking subsets of cells during the fast movements associated with
gastrulation.
KEY WORDS: Nanobody, Nucleus, GFP maturation, Live
in vivo
imaging, Cell tracking,
Drosophila melanogaster
INTRODUCTION
The first fluorescent proteins (FPs) were derived from jellyfish in the
1960s (Shimomura et al., 1962) and quickly revolutionized biology
(Kremers et al., 2011). Since their discovery, many other variants
have been extracted and engineered to improve brightness, widen
the colour palette and reduce maturation time (Balleza et al., 2018).
The last is especially important during live imaging of transient cell
types, and often sets a limitation regarding how quickly a signal can
be observed. Options are further restricted with deep-tissue imaging,
which requires use of two-photon microscopy commonly supported
by a standard Ti:sapphire laser that can be used to excite FPs
efficiently in the 700-1000 nm range (Drobizhev et al., 2011;
Moulton, 1986). This wavelength range is able to excite a more-
limited range of fluorophores, with most having longer maturation
times (Drobizhev et al., 2011; Piatkevich and Verkhusha, 2011). At
32°C, 90% of EGFP maturation occurs in about 1 h, with mRFP1
taking a bit longer at
80 min and mCerulean a bit faster at
50 min
(Balleza et al., 2018). At 37°C, 50% maturation is reported by
2.5 h for mOrange and
1 h for dTomato (Shaner et al., 2004),
whereas dsRed versions maturation half-times are in a wide time
window ranging from 0.7 to 11 h (Bevis and Glick, 2002).
Maturation times vary depending on many factors but generally
take longer at lower temperature, such as the 25°C rearing
temperature of
Drosophila melanogaster
.
Current genetic methods for cell labelling, as well as emerging
image acquisition and analysis methods, now make it possible to
track the movement of individual cells in living organisms
(McMahon et al., 2008; Progatzky et al., 2013; Stegmaier et al.,
2016) and to automatically track differentially FP-labelled cells to
reconstruct cellular level interactions (Prasad and Montell, 2007;
Smutny et al., 2017; Theveneau et al., 2013). However, many
developmental processes are short-lived, which makes labelling
cells a challenging task. For example, mesoderm spreading in
Drosophila
takes only about 1 h (see Fig. 2A; Clark et al., 2011;
Leptin, 2005; Sun et al., 2020) and even with a strong mesoderm-
specific zygotic driver, such as the
twist
(
twi
) enhancer, GFP
expression is not detectable early enough to illuminate the process
from its beginning (Clark et al., 2011).
To capture mRNA synthesis
the earliest readout of the activity
of a gene
live, the MS2-MCP system (Bertrand et al., 1998; Forrest
and Gavis, 2003) was tailored for use in
Drosophila
(Garcia et al.,
2013; Lucas et al., 2013). This system overcomes the challenge of
waiting for FP maturation by depositing the parts of the system
carrying the FP maternally (i.e. MCP-FP fusion proteins). However,
this system is not particularly suitable for cell tracking as MCP-FP
signal associated with nascent transcription presents as small spots
within the nucleus, which does not support robust identification or
tracking of individual nuclei over time, especially if cells are
moving. We therefore focused our approach on alternative methods
that permit labelling of entire nuclei.
Nanobodies (Hamers-Casterman et al., 1993) are small single-
domain antibodies usually derived from camelids or sharks
(Könning et al., 2017); designed ankyrin repeat proteins
(DARPins) are small synthetic proteins that also can engage in
selected binding. When these molecules, nanobodies or DARPins,
are fused to FPs, they have been used successfully to label specific
cell compartments, both in cell culture (Rothbauer et al., 2006) and
in living organisms (Harmansa and Affolter, 2018). Nanobodies
have also been applied in assays to sequester, and therefore interfere
with, secreted protein distribution, indicating that they are indeed
robust and specific binders (Harmansa et al., 2015). In addition,
these molecules have also been useful in tracking subcellular
translocation events; e.g. in mammalian cell culture, GFP nanobody
was fused to a nuclear localization sequence (NLS) (Kalderon et al.,
1984) and used to detect translocation of another GFP-fusion
protein into the nucleus (Kirchhofer et al., 2010). In a more recent
study in
Drosophila
, CRISPR gene editing was used to generate
FP-specific nanobody fused to transcription factors (TFs), thereby
Handling Editor: Thomas Lecuit
Received 24 May 2021; Accepted 12 July 2021
California Institute of Technology, 1200 East California Blvd, Pasadena, CA 91125,
USA.
*Author for correspondence (angelike@caltech.edu)
Z.Á., 0000-0002-4587-6212; L.D., 0000-0002-1739-6537; A.S., 0000-0001-
6597-2036
This is an Open Access article distributed under the terms of the Creative Commons Attribution
License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use,
distribution and reproduction in any medium provided that the original work is properly attributed.
1
© 2021. Published by The Company of Biologists Ltd
|
Development (2021) 148, dev199822. doi:10.1242/dev.199822
DEVELOPMENT
permitting observation of transcription factor dynamics live
(Bothma et al., 2018). This particular method also involved use
of maternally deposited FPs in embryos to overcome the limitation
imposed by maturation time of FPs in observing early zygotic
gene expression. As these TFs localize within the nucleus, the FPs
were also enriched in the nucleus, allowing labelling of nuclei in
early embryos but with the downside that making such TF-
nanobody fusion transgenic can be labour intensive and
challenging, with the added possibility that fusion proteins alter
TF function.
Here, we introduce a new nanobody-based labelling method to
Drosophila
, in which a GFP-specific nanobody tagged with three
nuclear localization sequences (3×NLSs) draws GFP into the
nucleus, permitting efficient cell labelling
in vivo
. This Nanobody
Nuclear Trap (NaNuTrap, NNT) is expressed using the existing
Drosophila
Gal4/UAS (Brand and Perrimon, 1993) system and can
therefore be used to label specific cell groups using existing Gal4
lines without additional genetic modification. When coupled with
fly stocks that maternally deposit GFP, this new tool accelerates
nuclear labelling in early embryos by eliminating the lag usually
associated with FP maturation. NaNuTrap also has the potential to
convert cytoplasmic GFP expressed in any extant transgenic fly line
into a nuclear label, again without additional genetic modification.
RESULTS AND DISCUSSION
NaNuTrap allows early labelling of nuclei
in vivo
We have created a UAS reporter line (5×UAS) that expresses a GFP
nanobody (Bothma et al., 2018; Kirchhofer et al., 2010) fused to a
3×NLS sequence (Chertkova et al., 2020) able to localize
maternally deposited GFP into the nucleus. Variables tested
Fig. 1. The NaNuTrap (NNT) method.
(A) The 5×UAS::NNT
construct: a GFP nanobody is fused to three nuclear localization
sequences (3×NLS) with a six aspartate (6D) linker sequence and
expression driven by the Gal4/UAS system (5×UAS). (B,C)
Schematic illustrating the NNT method. (B) Maternally deposited
GFP is distributed evenly in the embryo. (C) When the nanobody
construct NNT is also expressed, it drives the already matured
GFP into the cell nuclei. (D,E) Microscope stills from live
in vivo
imaging sessions in which the maternal driver Vasa is used to
deposit GFP (Vasa::EGFP, green) in a His2Av-mRFP1 labelled
(red) embryo (stage 10) (D); however, nuclear signal from GFP
(green) is observed only when co-expressed with the NNT
construct (E). (F) In the presence of maternally deposited GFP, the
NNT construct, when expressed zygotically in the mesoderm and
midline using the Twi-Gal4 driver, can localize GFP into the nuclei
of the presumptive mesoderm cells in the cellular blastoderm (F,
stage 5). (G,G
) Two-photon images of the collapsing furrow (stage
7/8); embryo genotype is the same as in F. Longitudinal section
(G) and cross-section (G
) of the same 3D reconstructed image.
Yolk autofluorescence appears in yellow, while signal from GFP-
expressing nuclei is green. The rectangle in G indicates the
position of the cross-section view shown in G
. Arrow indicates
mesoderm cell nuclei. (H) Microscope still from a live
in vivo
imaging session (see Movie 4) of an embryo expressing NNT
using the En-Gal4 driver; EGFP was deposited maternally.
2
TECHNIQUES AND RESOURCES
Development (2021) 148, dev199822. doi:10.1242/dev.199822
DEVELOPMENT
include two different linker sequences [i.e. six aspartates (6D) and
six glycines (6G)] shown to differentially affect nuclear localization
(Martin et al., 2015), as well as the relative position of the NLS and
nanobody sequences. We found that the 6D and 6G linker sequences
work equivalently, whereas placement of the NLS at the N terminus
of the nanobody sequence drives nuclear localization more
efficiently than when it was placed at the C terminus (data not
shown). The construct, N-terminal 3×NLS followed by a 6D linker
and fused to GFP nanobody, is henceforth referred to as NaNuTrap
(NNT) (Fig. 1A).
A fly line was generated that expresses GFP maternally through
Vasa::EGFP (Bothma et al., 2018) and carries the NNT construct
(5×UAS::NNT; Vasa::EGFP); this line was combined with various
Gal4 drivers to support targeted expression of NNT using standard
crosses. In embryos derived from this cross, we found that our NNT
construct efficiently transfers maternal GFP into the nucleus in a
GAL4-dependent manner (Fig. 1E,F), thereby making nuclei bright
enough to detect the tissue-specific expression of different drivers
live through confocal (Fig. 1E,F,H; e.g. see Movie 1) and two-
photon microscopy (Fig. 1G,G
).
To evaluate the timing and intensity of the signal from this new
NNT system versus traditional labelling approaches, we drove either
NNT (with maternally deposited GFP) or a standard NLS-GFP
transgene with the same GAL4 and compared the nuclear
fluorescent signal (Fig. 2, Fig. S1). Embryos were imaged live
(Movies 1 and 2) and the change in fluorescent intensity over time
was measured (see Materials and Methods). Two different Gal4
drivers that are expressed during gastrulation (Fig. 2A-A
′′
) were
used to express either NNT or NLS-GFP: a mesoderm-specific
Gal4, 2xPE-Gal4 (Fig. 2B-D); and an ectoderm-specific Gal4, Kr-
Gal4 (Fig. 2E-G). We were able to observe fluorescent signals
31 min and 15 min earlier using the NNT system with 2×PE- and
Kr-Gal4, respectively, when compared with using the NLS-GFP
(see Materials and Methods). The intensity of the signal driven by
the NNT system also increased more quickly. NLS-GFP signal was
only able to match the intensity level of the NNT about 60 min after
initial detection of the NNT signal (60 and 59 min for the 2×PE and
Kr drivers, respectively; see supplementary Materials and
Methods). Many developmental processes are very fast, e.g.
mesoderm spreading, which takes only about 1 h to complete
(Sun et al., 2020). As live imaging is ideal for such quick, dynamic
processes, the ability to rapidly increase fluorescent signal while
labelling specific cell groups is crucial.
This method could also be used to generate marked balancer
lines, usable at earlier stages, by introducing the NNT and Vasa::
EGFP constructs into existing, widely used fluorescent balancer
chromosome lines used in
Drosophila
, thereby permitting
discernment of homozygous versus heterozygous genotypes.
Fig. 2. NaNuTrap-driven fluorescent signal precedes NLS-GFP signal.
(A-A
′′
) Schematic of the developmental stages corresponding to snapshots from
movies shown in the same column: stage 5, cellularization (A); stage 7, furrow closure (A
); and stage 9, mesoderm spreading (A
′′
). (B-C
′′
) Snapshots from
live movies: 2×PE-Gal4 driver is used to label mesoderm by driving the expression of the NNT construct (B-B
′′
) or a NLS-GFP construct (C-C
′′
). NNT-driven
signal appears during invagination, whereas NLS-GFP signal is still not detectable at furrow closure. Movies start 25 min before ventral furrow clos
ure.
(D) Mean intensity of GFP signal shown over time. NNT-driven signal intensity is shown in blue; NLS-GFP is shown in green (
n
=3 embryos, error bars
indicate s.e.m). (E-F
′′
) Snapshots from live movies: Kr-Gal4 driver is used to label ectodermal cells by driving the expression of the NNT construct (E-E
′′
)ora
NLS-GFP construct (F-F
′′
). Movies start 40 min before furrow closure. (G) Mean intensity of GFP signal shown over time. NNT-driven signal intensity is
shown in blue; NLS-GFP is shown in green (
n
=3 embryos, error bars indicate s.e.m). All curves (D,G) were shifted in time such that t=0 corresponds to
furrow closure. Time is shown in hh:mm:ss format in movie snapshots.
3
TECHNIQUES AND RESOURCES
Development (2021) 148, dev199822. doi:10.1242/dev.199822
DEVELOPMENT
Balancers using Kr-Gal4, such as TKG (TM3, Kr-Gal4 UAS-GFP),
are detectable at stage 9 (Casso et al., 2000); those using Twi-Gal4
and 2×EGFP (Halfon et al., 2002) are detectable earlier, at stage 8
(Movie 3). As shown in Movie 3, upon co-expression of NNT and
maternally loaded GFP, presence of these balancers can be detected
earlier, during cellularization at stage 5. This is highly advantageous
for distinguishing zygotic genotypes using live-imaging
approaches.
Our analysis also revealed how the maternal GFP signal, the
limiting factor for NNT functionality, plateaus over time (Fig. 2D,
G). We tested a number of Gal4 lines (Fig. S2, Movie 4) and found
that the NNT system also worked with drivers expressed after
gastrulation, e.g. En-Gal4 (Fig. S2, Movie 4). Using the NNT
system to visualize En-Gal4 action, we found that expression was
initiated starting at stage 9 following germ band extension. This
indicates that, although the NNT uses maternally deposited GFP,
the label perdures into embryonic development and can therefore
be used to speed up and intensify visualization of expression
supported at later stages. Nevertheless, because zygotically
expressed NLS-GFP can potentially accumulate over time,
depending on the specific driver, it would be ideal to have both
maternally deposited and zygotically expressed fluorophores
available to study longer developmental processes. For such
purposes, we generated a fly stock containing both the NNT
system and the NLS-GFP transgene, which combines the advantage
of early nuclear labelling with ongoing accumulation of nuclear
GFP signal over time (Movie 5).
Other applications: making cytoplasmic GFP nuclear with
NaNuTrap
In addition to early nuclear labelling, we also investigated whether
the NNT construct could be used to transfer not only a limited
amount of maternal GFP but also the potentially larger amounts of
GFP expressed by some zygotic drivers into the nucleus. This would
allow one to convert the cytoplasmic GFP expressed in any of the
thousands of available transgenic fly lines into a nuclear marker by
simply crossing them to NNT, thereby facilitating counting or
tracking of cells without any genetic modification necessary. In
order to test this, we crossed the NNT line to the TTG balancer
stock, which expresses cytoplasmic GFP in the mesoderm,
producing a very bright signal (TM3, twi>Gal4 UAS-2×EGFP)
(Fig. 3A,A
; Halfon et al., 2002). Indeed, we find that the NNT
construct can efficiently transfer sufficient amounts of GFP into the
nucleus to permit nuclear detection in the mesoderm (Fig. 3B,B
).
This nuclear enrichment functionality could also be used to
concentrate otherwise weak or diffuse cytoplasmic GFP signal
and permit more-robust identification of expressing cells or to
distinguish them from auto-fluorescence.
The availability of
Drosophila
lines expressing fluorescently
tagged proteins generated through enhancer trapping (Kelso et al.,
2004; Nagarkar-Jaiswal et al., 2015; Singari et al., 2014), user-
defined FP reporter constructs (e.g. Kudron et al., 2018) and
homologous recombination or CRISPR-mediated introduction of
FP-tags into genes (Dunst et al., 2015; Li-Kroeger et al., 2018) is
growing exponentially. Many of these fluorescent reporters are
cytoplasmically expressed and the signal is too low to be detectable
live, or too diffuse to be reliably localized in fixed tissues. To
demonstrate how the NNT system can enhance the utility of GFP
reporters in such scenarios, we turned to a large GFP reporter
construct we constructed in a previous study designed to capture
expression of the gene
brinker
(
brk
) in the early embryo (Dunipace
et al., 2013). We recently found that this construct also supports
expression of
brk
in the ovary, which is a dense collection of
interconnected cells, and native reporter GFP expression was
present throughout the germarium in a diffuse pattern (Fig. 3C). By
driving NNT with Tj-Gal4, which is expressed in the somatic
follicle cells that encapsulate the developing germline, cytoplasmic
GFP is efficiently concentrated in nuclei, permitting identification
Fig. 3. NaNuTrap efficiently transfers
zygotically expressed cytoplasmic FP into the
nucleus
in vivo
.
(A) TTG (Twi-Gal4, UAS-
2×EGFP) balancer line embryo at stage 11. A
large amount of EGFP is expressed in mesoderm
derivative cells. (A
) Higher magnification of the
area indicated in A. (B) Additional to cytoplasmic
EGFP, NNT is also expressed by the Twi-Gal4
driver of the TTG (TM3, Twi-Gal4, UAS-2×EGFP)
construct in the embryo shown at stage 11. (B
)
Higher magnification of the area indicated in
B. (C) Fixed tissue sample of germarium, anterior
to the left, showing GFP expression driven by the
large reporter construct BrkNFGFP (green;
Dunipace et al., 2013) and nuclei stained with
DAPI (blue). (D) The BrkNFGFP reporter
combined with NNT and a pan follicle cell driver,
Tj-Gal4, show distinct nuclear localization of GFP
within cells.
4
TECHNIQUES AND RESOURCES
Development (2021) 148, dev199822. doi:10.1242/dev.199822
DEVELOPMENT
and counting of follicle cells that was not previously possible with
this reporter (Figs 3D and 4A). NNT would similarly be expected to
facilitate visualization of the large number of endogenously GFP-
labelled
Drosophila
proteins (Sarov et al., 2016; Kina et al., 2019)
by altering the location of the endogenous proteins to support early
nuclear localization; however, doing so could affect protein function
and cause mutant phenotypes.
Concluding remarks
We developed NaNuTrap, specifically, for automated cell
segmentation and tracking to investigate cellular movements in
the fast-developing early
Drosophila
embryo. To determine
whether NaNuTrap-generated signal permits cell tracking
analyses, we captured movies of live embryos in which NNT
expression was driven by 2×PE and Kr-Gal4 (Fig. 4B,C; Movies 1
Fig. 4.
See next page for legend.
5
TECHNIQUES AND RESOURCES
Development (2021) 148, dev199822. doi:10.1242/dev.199822
DEVELOPMENT
and 2) and tested whether automatic segmentation could be applied
to NNT-localized, maternally deposited GFP. Although signal
initiation was variable at the cellularization stage (Fig. 4D,E),
segmentation became reliable shortly thereafter
by the time the
mesoderm invaginates (i.e. furrow formation) (Fig. 4D
,E
)
and
remained suitable for automatic tracking (Fig. 4E
′′
, Movie 6)
throughout the fast phase of germ band elongation. During this time
period, signal has proved to be insufficient in the past to support
segmentation and tracking using conventional labelling techniques
for these cell types (Fig. 2C
,F
, Movies 1 and 2). In addition, cells
lose signal during cell division but quickly regain it (Fig. 4F-F
′′′′′
,
Movie 7). Although an additional implication of this result is that
cell lineages cannot be followed solely using the NNT-based
labelling approach, we found that NNT also helps visualize cell
division irregularities in the labelled tissue (Fig. 4G-G
′′
). While cell
division phases of individual cells can be determined based on
histone labelling (Fig. 4G), tissue level patterns became more
apparent when the NNT signal was superimposed (Fig. 4G
′′
). For
lineage tracing, a similar method could potentially be developed in
which, instead of the 3×NLS, a histone protein or a histone
nanobody (Jullien et al., 2016) is fused to the GFP nanobody such
that it promotes association with the chromatin during cell division
and supports continuous tracking.
In summary, we have taken an approach that uses NLS and a
nanobody to localize GFP into the nucleus, and have applied it,
together with a source of maternally deposited GFP, to use for
in
vivo
labelling in
Drosophila
. In order to make it easily adaptable, we
created our NNT transgenic lines as UAS reporter lines to facilitate
labelling of different cell types. We were able to accelerate labelling
compared with traditional labelling methods that rely on zygotic
GFP expression, as labelling initiated 15-30 min earlier and for the
first hour was brighter. Our method based on the UAS system can be
used with the available large number of existing Gal4 lines through
a single cross, in a matter of a couple of weeks; a related approach,
LlamaTag, is more complicated, as creation of nanobody fusions to
transcription factors (or other proteins) takes much longer, is labour
intensive and also may affect function of the tagged endogenous
protein. Another way to tackle the problem of long FP maturation
times is to develop fast-maturing FPs. Recently, a method was
described in which fast-maturing FP mNeonGreen was fused to
NLSs and other sequences that boost efficiency of translation, and
used to assay early zygotic expression in
Drosophila
embryos
(Ceolin et al., 2020). This other approach is still dependent on the
maturation time of the FP (for mNeonGreen has a maturation half
time of
7-10 min), is currently not compatible with the Gal4
system and is less suitable for 2P imaging than GFP (Molina et al.,
2017); however, it has an advantage over NNT in that it is not
dependent on maternal GFP. The NNT approach presented here has
the potential to be expanded to other fluorescent proteins with
favourable spectral properties but the longer maturation times of
which have precluded their use for live imaging in the past. In
addition, in combination with existing Gal4-based FP balancer
lines, NNT allows earlier visualization of commonly used FP
balancer chromosomes to facilitate their use in younger embryos.
Last, the created NNT lines are capable of the transformation of GFP
expressed in existing transgenic fly lines, from a cytoplasmic into a
nuclear marker that allows for cell segmentation that would not be
possible with currently available methods. Here, we have focused on
how NNT can support cell tracking and earlier visualization of FPs
in embryos but similar
fast-tracking
of FP can be supported at later
stages in
Drosophila
, as well as in other animals that support the
Gal4-UAS expression system (Distel et al., 2009; Ovchinnikov
et al., 2008; Wang et al., 2017).
MATERIALS AND METHODS
Drosophila
stocks and genetics
5×UAS::NNT, this study, was combined with Vasa::EGFP (Bothma et al.,
2018) to create
5×UAS::NNT; Vasa::EGFP
. Virgins were collected from
5×UAS::NNT; Vasa::EGFP
and crossed to males from 2
×
PE (
y[1] w[*];
P{w[+mC]=GAL4-twi.2xPE}2
,:BDSC_2517), Kr (
P{w[+mC]=GAL4-
Kr.C}10o/TM3
,Sb[1], BDSC_58800), En (
w[*]; P{w[+mC]=stg-
lacZ.beta-E2.2}2; P{w[+m*]=en-GAL4.U}3,
BDSC_83350), Prd (
w[*];
P{w[+mW.hs]=GAL4-prd.F}RG1/TM3, Sb[1]
, BDSC_1947) or Hairy
(
w[*]; P{w[+mW.hs]=GawB}h[1J3],
RRID:BDSC_1734) Gal4 lines and
to TTG balancer line (
TM3,Twi-Gal4,2×EGFP
) (Halfon et al., 2002). For
flies expressing only NLS-GFP, virgins were collected from UAS::NLS-
GFP (
w[1118]; P{w[+mC]=UAS-GFP.nls}8
, BDSC_4776) and crossed to
2
×
PE-Gal4 or Kr-Gal4 males. To express both NNT and NLS-GFP in the
mesoderm, fly stocks
2×PE-Gal4; Vasa::EGFP
and
5xUAS::NNT; UAS::
NLS-GFP
were created. Embryos were collected from crossing virgins
from
2×PE; Vasa::EGFP
with males from the
5×UAS::NNT; UAS::NLS-
GFP
line. To visualize cell division, we generated line
His2Av-mRFP1;
NNT, Vasa::EGFP
(using line
w[*]; P{w[+mC]=His2Av-mRFP1}II.2
,
BDSC_23551), crossed it to the Twi-Gal4 (
P{w[+mC]=GAL4-
twi.G}108.4, w[1],
BDSC_914) driver, and collected the resulting
embryos. The BrkNFGFP fly stock has been published previously
(Dunipace et al., 2013).
Embryo handling and live imaging
Embryos were manually dechlorinated and mounted on a heptane glue slide
in a drop of water. Confocal images were recorded using a Zeiss Axio
Imager Z2 confocal microscope either with a C-Apochromat 40×/1.2 NA or
with a LCI Plan-Neofluar 25×/0.8 NA water dipping objective lens. Images
were taken in 15-20
z
-stacks separated by 3 μm. Videos were acquired with a
frame rate between 75 and 93 s. GFP was excited at 488 nm with 4-5% of
laser power. To visualize yolk and separate it from GFP signal, some movies
were also excited at 561 nm with 1% laser power. Gain was set to 550 V, the
pinhole to 100 μm and pixel dwell time to 0.76 or 1.52 μs. Images analysed
in Fig. 2 were recorded with the same settings. Two-photon images were
recorded using a Zeiss LSM 880 microscope and a LCI Plan-Neofluar
25×/0.8 NA water dipping objective lens. GFP was excited at 860 nm
Fig. 4. Cell nuclei segmentation and tracking in embryos labelled using
the NaNuTrap method.
(A) Cell nuclei segmented automatically, in an
immunostained tissue sample of the germarium from a BrkNFGFP reporter
line that was turned into a nuclei-labelled line with NNT. (B,C) Kr expression
pattern in early stage 7 embryo: snapshot from live movie of an embryo
labelled with NNT using the Kr-Gal4 driver (B) and a schematic of
expression of Kru
̈
ppel (Kr) (blue) and 2×PE/
twist
(purple) (C). (D,D
) Cell
nuclei segmented in live movies of embryos that are expressing NNT using
the 2×PE-Gal4 driver to label mesoderm and midline (Movie 1) at stage 5
cellularization (D) and at furrow closure (D
). (E-E
′′
) Cell nuclei segmented in
live movies of embryos that are expressing NNT using the Kr-Gal4 driver to
label ectodermal cells (Movie 2) at stage 5 cellularization (E) and at furrow
closure (E
). Cells are also tracked automatically during the time period
when conventional cell labelling methods are not labelling cell nuclei
efficiently (Movie 2). Colour bar indicates the average speed of the tracks
(0-4.5 μm/min) (E
′′
). Cells were segmented and tracked automatically using
Imaris spot detection and tracking functions. (F-F
′′′′′
) Loss of NNT signal is
an indicator of cell division. Snapshot from a live movie (Movie 7) of a
His2Av-mRFP1 embryo (stage 10) labelled with NNT using the Twi-Gal4
driver, showing a cell before division (F), in different cell division phases
metaphase (F
,F
′′
), anaphase (F
′′′
) and telophase (F
′′′′
)
and after division
(F
′′′′′
). Arrows indicate the same cell (wide arrow) and its daughter cells (two
thin arrows). (G-G
′′
) Cell division pattern in the mesoderm of a
Drosophila
embryo (stage 10) during the 3rd mesodermal division. Genotype is the
same as described in F. When NNT labelling is shown alone (G
)or
superimposed onto His2Av-mRFP1 signal (G
′′
), tissue level division patterns
can be visualized in the mesoderm. EGFP was deposited maternally in
embryos shown in B,D-G
′′
.
6
TECHNIQUES AND RESOURCES
Development (2021) 148, dev199822. doi:10.1242/dev.199822
DEVELOPMENT
with 7% of laser power, two times averaging and 2.06 μs pixel time, and
detected using the NDD detector. 102
z
-stacks were taken separated by
1.1 μm.
Ovary staining
Ovaries were collected and stained with GFP antibody (Rockland, 600-101-
215, 1:5000) and anti-goat 488 (Life Technologies, A11055, 1:400) as
described previously (Zimmerman et al., 2013), and mounted in SlowFade
Gold with DAPI (Invitrogen, S36939).
Plasmid construction and injection
NNT design includes three copies of an NLS sequence (PKKKRKV)
(Kalderon et al., 1984) linked with aspartic acid residues to create 3×NLS [as
previously described by Chertkova et al. (2020)]. The 3×NLS was fused
with a 6D or 6G linker to codon optimized GFP nanobody (Bothma et al.,
2018). For further details of the design, see the supplementary Materials and
Methods.
Image processing and data analysis
Images were analyzed using ImageJ.
Z
-projections of the 20
z
-stacks of the
green channel were created using the sum slices function. Images were
rotated by marking the midline and rotating into a horizontal position with
the head pointing to the left. Background was subtracted using the rolling
ball function with 50 μm radius. A rectangle was selected and placed close to
the midline while avoiding yolk signal (120×60 μm for 2×PE-Gal4, and
40×140 μm for Kr-Gal4) to measure and plot the mean intensity value
versus time curve using the Plot
z
-axis profile command. The resulting
intensity/time curves were shifted in time, such that the 0 time point
corresponded to furrow closure (determined based on the brightfield and
green channel images) and 0 intensity corresponded to minimal intensity
detected. For details, see the supplementary Materials and Methods.
Acknowledgements
We thank Jacques Bothma for helpful discussion, the Hernan Garcia lab for fly lines
and technical advice, the Caltech Beckman Imaging centre for support, Frank
Macabenta for helping with schematics, Susan Newcomb for comments on the
manuscript, and the anonymous reviewers for helpful suggestions.
Competing interests
The authors declare no competing or financial interests.
Author contributions
Conceptualization: Z.Á., L.D., A.S.; Methodology: Z.Á., L.D.; Validation: Z.Á., A.S.;
Formal analysis: Z.Á.; Investigation: Z.Á.; Resources: A.S.; Data curation: Z.Á.;
Writing - original draft: Z.Á.; Writing - review & editing: Z.Á., L.D., A.S.; Visualization:
Z.Á.; Supervision: A.S.; Project administration: A.S.; Funding acquisition: Z.Á., A.S.
Funding
This work was funded by the National Institutes of Health (R35GM118146 to A.S.)
and by the American Heart Association (18POST34080493 to Z.Á.). Open Access
funding provided by California Institute Of Technology. Deposited in PMC for
immediate release.
References
Balleza, E., Kim, J. M. and Cluzel, P.
(2018). Systematic characterization of
maturation time of fluorescent proteins in living cells.
Nat. Methods
15
, 47-51.
doi:10.1038/nmeth.4509
Bertrand, E., Chartrand, P., Schaefer, M., Shenoy, S. M., Singer, R. H. and Long,
R. M.
(1998). Localization of ASH1 mRNA particles in living yeast.
Mol. Cell
2
,
437-445. doi:10.1016/S1097-2765(00)80143-4
Bevis, B. J. and Glick, B. S.
(2002). Rapidly maturing variants of the Discosoma red
fluorescent protein (DsRed).
Nat. Biotechnol.
20
, 83-87. doi:10.1038/nbt0102-83
Bischof, J., Maeda, R. K., Hediger, M., Karch, F. and Basler, K.
(2007). An
optimized transgenesis system for Drosophila using germ-line-specific phiC31
integrases.
Proc. Natl. Acad. Sci. USA
104
, 3312-3317. doi:10.1073/pnas.
0611511104
Bothma, J. P., Norstad, M. R., Alamos, S. and Garcia, H. G.
(2018). LlamaTags: a
versatile tool to image transcription factor dynamics in live embryos.
Cell
173
,
1810-1822.e16. doi:10.1016/j.cell.2018.03.069
Brand, A. H. and Perrimon, N.
(1993). Targeted gene expression as a means of
altering cell fates and generating dominant phenotypes.
Development
118
,
401-415. doi:10.1242/dev.118.2.401
Casso, D., Ram
ı
́
rez-Weber, F.-A. and Kornberg, T. B.
(2000). GFP-tagged
balancer chromosomes for Drosophila melanogaster.
Mech. Dev.
91
, 451-454.
doi:10.1016/S0925-4773(00)00248-3
Ceolin, S., Hanf, M., Bozek, M., Storti, A. E., Gompel, N., Unnerstall, U., Jung, C.
and Gaul, U.
(2020). A sensitive mNeonGreen reporter system to measure
transcriptional dynamics in Drosophila development.
Commun. Biol.
3
663.
doi:10.1038/s42003-020-01375-5
Chertkova, A. O., Mastop, M., Postma, M., van Bommel, N., van der Niet, S.,
Batenburg, K. L., Joosen, L., Gadella, T. W. J., Okada, Y. and Goedhart, J.
(2020). Robust and bright genetically encoded fluorescent markers for
highlighting structures and compartments in mammalian cells.
bioRxiv.
doi:10.
1101/160374
Clark, I. B. N., Muha, V., Klingseisen, A., Leptin, M. and Mu
̈
ller, H.-A. J.
(2011).
Fibroblast growth factor signalling controls successive cell behaviours during
mesoderm layer formation in Drosophila.
Development
138
, 2705-2715. doi:10.
1242/dev.060277
Distel, M., Wullimann, M. F. and Ko
̈
ster, R. W.
(2009). Optimized Gal4 genetics for
permanent gene expression mapping in zebrafish.
Proc. Natl. Acad. Sci. USA
106
, 13365-13370. doi:10.1073/pnas.0903060106
Drobizhev, M., Makarov, N. S., Tillo, S. E., Hughes, T. E. and Rebane, A.
(2011).
Two-photon absorption properties of fluorescent proteins.
Nat. Methods
8
,
393-399. doi:10.1038/nmeth.1596
Dunipace, L., Saunders, A., Ashe, H. L. and Stathopoulos, A.
(2013).
Autoregulatory feedback controls sequential action of cis-regulatory modules at
the brinker locus.
Dev. Cell
26
, 536-543. doi:10.1016/j.devcel.2013.08.010
Dunst, S., Kazimiers, T., von Zadow, F., Jambor, H., Sagner, A., Brankatschk,
B., Mahmoud, A., Spannl, S., Tomancak, P., Eaton, S. et al.
(2015).
Endogenously tagged rab proteins: a resource to study membrane trafficking in
Drosophila.
Dev. Cell
33
, 351-365. doi:10.1016/j.devcel.2015.03.022
Forrest, K. M. and Gavis, E. R.
(2003). Live imaging of endogenous RNA reveals a
diffusion and entrapment mechanism for nanos mRNA localization in Drosophila.
Curr. Biol.
13
, 1159-1168. doi:10.1016/S0960-9822(03)00451-2
Garcia, H. G., Tikhonov, M., Lin, A. and Gregor, T.
(2013). Quantitative imaging of
transcription in living Drosophila embryos links polymerase activity to patterning.
Curr. Biol.
23
, 2140-2145. doi:10.1016/j.cub.2013.08.054
Halfon, M. S., Gisselbrecht, S., Lu, J., Estrada, B., Keshishian, H. and
Michelson, A. M.
(2002). New fluorescent protein reporters for use with the
Drosophila Gal4 expression system and for vital detection of balancer
chromosomes.
Genesis
34
, 135-138. doi:10.1002/gene.10136
Hamers-Casterman, C., Atarhouch, T., Muyldermans, S., Robinson, G.,
Hamers, C., Songa, E. B., Bendahman, N. and Hamers, R.
(1993). Naturally
occurring antibodies devoid of light chains.
Nature
363
, 446-448. doi:10.1038/
363446a0
Harmansa, S. and Affolter, M.
(2018). Protein binders and their applications in
developmental biology.
Development
145
, dev148874. doi:10.1242/dev.148874
Harmansa, S., Hamaratoglu, F., Affolter, M. and Caussinus, E.
(2015). Dpp
spreading is required for medial but not for lateral wing disc growth.
Nature
527
,
317-322. doi:10.1038/nature15712
Jullien, D., Vignard, J., Fedor, Y., Be
́
ry, N., Olichon, A., Crozatier, M., Erard, M.,
Cassard, H., Ducommun, B., Salles, B. et al.
(2016). Chromatibody, a novel
non-invasive molecular tool to explore and manipulate chromatin in living cells.
Development
143
, e1.2. doi:10.1242/dev.141804
Kalderon, D., Roberts, B. L., Richardson, W. D. and Smith, A. E.
(1984). A short
amino acid sequence able to specify nuclear location.
Cell
39
, 499-509. doi:10.
1016/0092-8674(84)90457-4
Kelso, R. J., Buszczak, M., Quin
̃
ones, A. T., Castiblanco, C., Mazzalupo, S. and
Cooley, L.
(2004). Flytrap, a database documenting a GFP protein-trap insertion
screen in Drosophila melanogaster.
Nucleic Acids Res.
32
, D418-D420. doi:10.
1093/nar/gkh014
Kina, H., Yoshitani, T., Hanyu-Nakamura, K. and Nakamura, A.
(2019). Rapid
and efficient generation of GFP-knocked-in Drosophila by the CRISPR-Cas9-
mediated genome editing.
Dev. Growth Differ.
61
, 265-275. doi:10.1111/dgd.
12607
Kirchhofer, A., Helma, J., Schmidthals, K., Frauer, C., Cui, S., Karcher, A.,
Pellis, M., Muyldermans, S., Casas-Delucchi, C. S., Cardoso, M. C. et al.
(2010). Modulation of protein properties in living cells using nanobodies.
Nat.
Struct. Mol. Biol.
17
, 133-138. doi:10.1038/nsmb.1727
Ko
̈
nning, D., Zielonka, S., Grzeschik, J., Empting, M., Valldorf, B., Krah, S.,
Schro
̈
ter, C., Sellmann, C., Hock, B. and Kolmar, H.
(2017). Camelid and shark
single domain antibodies: structural features and therapeutic potential.
Curr. Opin.
Struct. Biol.
45
, 10-16. doi:10.1016/j.sbi.2016.10.019
Kremers, G.-J., Gilbert, S. G., Cranfill, P. J., Davidson, M. W. and Piston, D. W.
(2011). Fluorescent proteins at a glance.
J. Cell Sci.
124
, 157-160. doi:10.1242/
jcs.072744
Kudron, M. M., Victorsen, A., Gevirtzman, L., Hillier, L. W., Fisher, W. W.,
Vafeados, D., Kirkey, M., Hammonds, A. S., Gersch, J., Ammouri, H. et al.
7
TECHNIQUES AND RESOURCES
Development (2021) 148, dev199822. doi:10.1242/dev.199822
DEVELOPMENT
(2018). The ModERN resource: genome-wide binding profiles for hundreds of and
transcription factors.
Genetics
208
, 937-949. doi:10.1534/genetics.117.300657
Leptin, M.
(2005). Gastrulation movements: the logic and the nuts and bolts.
Dev.
Cell
8
, 305-320. doi:10.1016/j.devcel.2005.02.007
Li-Kroeger, D., Kanca, O., Lee, P.-T., Cowan, S., Lee, M. T., Jaiswal, M., Salazar,
J. L., He, Y., Zuo, Z. and Bellen, H. J.
(2018). An expanded toolkit for gene
tagging based on MiMIC and scarless CRISPR tagging in.
Elife
7
, e38709. doi:10.
7554/eLife.38709.023
Lucas, T., Ferraro, T., Roelens, B., De Las Heras Chanes, J., Walczak, A. M.,
Coppey, M. and Dostatni, N.
(2013). Live imaging of bicoid-dependent
transcription in Drosophila embryos.
Curr. Biol.
23
, 2135-2139. doi:10.1016/j.
cub.2013.08.053
Martin, R. M., Ter-Avetisyan, G., Herce, H. D., Ludwig, A. K., La
̈
ttig-Tu
̈
nnemann,
G. and Cardoso, M. C.
(2015). Principles of protein targeting to the nucleolus.
Nucleus
6
, 314-325. doi:10.1080/19491034.2015.1079680
McMahon, A., Supatto, W., Fraser, S. E. and Stathopoulos, A.
(2008). Dynamic
analyses of Drosophila gastrulation provide insights into collective cell migration.
Science
322
, 1546-1550. doi:10.1126/science.1167094
Molina, R. S., Tran, T. M., Campbell, R. E., Lambert, G. G., Salih, A., Shaner, N.
C., Hughes, T. E. and Drobizhev, M.
(2017). Blue-shifted green fluorescent
protein homologues are brighter than enhanced green fluorescent protein under
two-photon excitation.
J. Phys. Chem. Lett.
8
, 2548-2554. doi:10.1021/acs.jpclett.
7b00960
Moulton, P. F.
(1986). Spectroscopic and laser characteristics of Ti:Al_2O_3.
J. Opt.
Soc. Am. B
3
, 125. doi:10.1364/JOSAB.3.000125
Nagarkar-Jaiswal, S., Lee, P.-T., Campbell, M. E., Chen, K., Anguiano-Zarate,
S., Gutierrez, M. C., Busby, T., Lin, W.-W., He, Y., Schulze, K. L. et al.
(2015). A
library of MiMICs allows tagging of genes and reversible, spatial and temporal
knockdown of proteins in Drosophila.
Elife
4
, e05338. doi:10.7554/eLife.05338.
023
Ovchinnikov, D. A., van Zuylen, W. J. M., DeBats, C. E. E., Alexander, K. A.,
Kellie, S. and Hume, D. A.
(2008). Expression of Gal4-dependent transgenes in
cells of the mononuclear phagocyte system labeled with enhanced cyan
fluorescent protein using Csf1r-Gal4VP16/UAS-ECFP double-transgenic mice.
J. Leukoc. Biol.
83
, 430-433. doi:10.1189/jlb.0807585
Piatkevich, K. D. and Verkhusha, V. V.
(2011). Guide to red fluorescent proteins
and biosensors for flow cytometry.
Methods Cell Biol.
102
, 431-461. doi:10.1016/
B978-0-12-374912-3.00017-1
Prasad, M. and Montell, D. J.
(2007). Cellular and molecular mechanisms of border
cell migration analyzed using time-lapse live-cell imaging.
Dev. Cell
12
, 997-1005.
doi:10.1016/j.devcel.2007.03.021
Progatzky, F., Dallman, M. J. and Lo Celso, C.
(2013). From seeing to believing:
labelling strategies for in vivo cell-tracking experiments.
Interface Focus
3
,
20130001. doi:10.1098/rsfs.2013.0001
Rothbauer, U., Zolghadr, K., Tillib, S., Nowak, D., Schermelleh, L., Gahl, A.,
Backmann, N., Conrath, K., Muyldermans, S., Cardoso, M. C. et al.
(2006).
Targeting and tracing antigens in live cells with fluorescent nanobodies.
Nat.
Methods
3
, 887-889. doi:10.1038/nmeth953
Sarov, M., Barz, C., Jambor, H., Hein, M. Y., Schmied, C., Suchold, D., Stender,
B., Janosch, S., Vikas, V. K. J., Krishnan, R. T. et al.
(2016). A genome-wide
resource for the analysis of protein localisation in Drosophila.
Elife
5
, e12068.
doi:10.7554/eLife.12068
Shaner, N. C., Campbell, R. E., Steinbach, P. A., Giepmans, B. N. G., Palmer,
A. E. and Tsien, R. Y.
(2004). Improved monomeric red, orange and yellow
fluorescent proteins derived from Discosoma sp. red fluorescent protein.
Nat.
Biotechnol.
22
, 1567-1572. doi:10.1038/nbt1037
Shimomura, O., Johnson, F. H. and Saiga, Y.
(1962). Extraction, purification and
properties of aequorin, a bioluminescent protein from the luminous
hydromedusan, Aequorea.
J. Cell. Comp. Physiol.
59
, 223-239. doi:10.1002/
jcp.1030590302
Singari, S., Javeed, N., Tardi, N. J., Marada, S., Carlson, J. C., Kirk, S., Thorn,
J. M. and Edwards, K. A.
(2014). Inducible protein traps with dominant
phenotypes for functional analysis of the Drosophila genome.
Genetics
196
,
91-105. doi:10.1534/genetics.113.157529
Smutny, M., Ákos, Z., Grigolon, S., Shamipour, S., Ruprecht, V., C
̌
apek, D.,
Behrndt, M., Papusheva, E., Tada, M., Hof, B. et al.
(2017). Friction forces
position the neural anlage.
Nat. Cell Biol.
19
, 306-317. doi:10.1038/ncb3492
Stegmaier, J., Amat, F., Lemon, W. C., McDole, K., Wan, Y., Teodoro, G., Mikut,
R. and Keller, P. J.
(2016). Real-time three-dimensional cell segmentation in
large-scale microscopy data of developing embryos.
Dev. Cell
36
, 225-240.
doi:10.1016/j.devcel.2015.12.028
Sun, J., Macabenta, F., Akos, Z. and Stathopoulos, A.
(2020). Collective
migrations of Drosophila embryonic trunk and caudal mesoderm-derived muscle
precursor cells.
Genetics
215
, 297-322. doi:10.1534/genetics.120.303258
Theveneau, E., Steventon, B., Scarpa, E., Garcia, S., Trepat, X., Streit, A. and
Mayor, R.
(2013). Chase-and-run between adjacent cell populations promotes
directional collective migration.
Nat. Cell Biol.
15
, 763-772. doi:10.1038/ncb2772
Wang, H., Liu, J., Gharib, S., Chai, C. M., Schwarz, E. M., Pokala, N. and
Sternberg, P. W.
(2017). cGAL, a temperature-robust GAL4-UAS system for
Caenorhabditis elegans.
Nat. Methods
14
, 145-148. doi:10.1038/nmeth.4109
Zimmerman, S. G., Peters, N. C., Altaras, A. E. and Berg, C. A.
(2013). Optimized
RNA ISH, RNA FISH and protein-RNA double labeling (IF/FISH) in Drosophila
ovaries.
Nat. Protoc.
8
, 2158-2179. doi:10.1038/nprot.2013.136
8
TECHNIQUES AND RESOURCES
Development (2021) 148, dev199822. doi:10.1242/dev.199822
DEVELOPMENT