Chemical & Biomedical Imaging,
Год журнала:
2023,
Номер
1(5), С. 403 - 413
Опубликована: Июнь 28, 2023
Multidimensional
single-molecule
localization
microscopy
(mSMLM)
represents
a
paradigm
shift
in
the
realm
of
super-resolution
techniques.
It
affords
simultaneous
detection
spatial
locations
at
nanoscale
and
functional
information
by
interrogating
emission
properties
switchable
fluorophores.
The
latter
is
finely
tuned
to
report
its
local
environment
through
carefully
manipulated
laser
illumination
strategies.
This
Perspective
highlights
recent
strides
mSMLM
with
focus
on
fluorophore
designs
their
integration
into
imaging
systems.
Particular
interests
are
accomplishments
multiplexed
imaging,
polarity
hydrophobicity
mapping,
orientational
imaging.
Challenges
prospects
also
discussed,
which
include
development
more
vibrant
fluorescent
probes,
optimization
optical
implementation
judiciously
utilize
photon
budget,
advancement
analysis
machine
learning
Biosensors,
Год журнала:
2025,
Номер
15(5), С. 283 - 283
Опубликована: Апрель 30, 2025
Single-molecule
fluorescence
technology
stands
at
the
forefront
of
scientific
research
as
a
sophisticated
tool,
pushing
boundaries
our
understanding.
This
review
comprehensively
summarizes
technological
advancements
in
single-molecule
detection,
highlighting
latest
achievements
development
fluorescent
probes,
imaging
systems,
and
biosensors.
It
delves
into
applications
these
cutting-edge
tools
drug
discovery
neuroscience
research,
encompassing
design
monitoring
complex
delivery
elucidation
pharmacological
mechanisms
pharmacokinetics,
intricacies
neuronal
signaling
synaptic
function,
molecular
underpinnings
neurodegenerative
diseases.
The
exceptional
sensitivity
demonstrated
underscores
vast
potential
modern
biomedical
heralding
its
expansion
other
domains.
Nano Letters,
Год журнала:
2024,
Номер
24(20), С. 6078 - 6083
Опубликована: Май 9, 2024
Gamma-prefoldin
(γPFD),
a
unique
chaperone
found
in
the
extremely
thermophilic
methanogen
Methanocaldococcus
jannaschii,
self-assembles
into
filaments
vitro,
which
so
far
have
been
observed
using
transmission
electron
microscopy
and
cryo-electron
microscopy.
Utilizing
three-dimensional
stochastic
optical
reconstruction
(3D-STORM),
here
we
achieve
∼20
nm
resolution
by
precisely
locating
individual
fluorescent
molecules,
hence
resolving
γPFD
ultrastructure
both
vitro
vivo.
Through
CF647
NHS
ester
labeling,
first
demonstrate
accurate
visualization
of
bundles
with
purified
γPFD.
Next,
implementing
immunofluorescence
labeling
after
creating
3xFLAG-tagged
strain,
successfully
visualize
M.
jannaschii
cells.
3D-STORM
two-color
STORM
imaging
DNA,
show
widespread
distribution
filamentous
structures
within
cell.
These
findings
provide
valuable
insights
structure
localization
γPFD,
opening
up
possibilities
for
studying
intriguing
nanoscale
components
not
only
archaea
but
also
other
microorganisms.
Abstract
Spectrally‐resolved
single
molecule
localization
microscopy
(srSMLM)
is
a
recent
technique
enriching
with
the
simultaneous
recording
of
spectra
emitters.
srSMLM
resolution
limited
by
number
photons
collected
per
Sharing
photon
budget
to
record
and
spectroscopic
information
results
in
loss
spatial
spectral
resolution—or
forces
sacrifice
one
at
expense
other.
Here,
srUnet—a
deep‐learning
Unet‐based
image
processing
routine
trained
increase
signals
compensate
for
inherent
additionally
component
reported.
Both
precision
are
improved
srUnet—particularly
low‐emitting
species.
srUnet
increases
fraction
whose
signal
can
be
both
spatially
spectrally
characterized.
It
preserves
shifts
linearity
dispersion
light.
strongly
facilitates
wavelength
assignment
multicolor
experiments.
simple
post‐processing
add‐on
boosting
performance
close
conventional
SMLM
potential
turn
into
new
standard
imaging.
Proceedings of the National Academy of Sciences,
Год журнала:
2024,
Номер
121(50)
Опубликована: Дек. 5, 2024
The
living
cell
creates
a
unique
internal
molecular
environment
that
is
challenging
to
characterize.
By
combining
single-molecule
displacement/diffusivity
mapping
(SM
d
M)
with
physiologically
active
extracts
prepared
from
Xenopus
laevis
eggs,
we
sought
elucidate
properties
of
the
cytoplasm.
Quantification
diffusion
coefficients
15
diverse
proteins
in
extract
showed
that,
compared
water,
negatively
charged
diffused
~50%
slower,
while
positively
was
reduced
by
~80
90%.
Adding
increasing
concentrations
salt
progressively
alleviated
suppressed
observed
for
proteins,
signifying
electrostatic
interactions
within
predominately
macromolecular
environment.
To
investigate
contribution
RNA,
an
abundant,
component
cytoplasm,
were
treated
ribonuclease,
which
resulted
low
diffusivity
domains
indicative
aggregation,
likely
due
liberation
RNA-binding
such
as
ribosomal
since
this
effect
could
be
mimicked
adding
polypeptides.
Interestingly,
under
typical
conditions
inhibit
actin
polymerization,
different
sizes
similar
suppression
consistent
our
separately
measured
2.22-fold
higher
viscosity
over
water.
Restoring
or
enhancing
polymerization
larger
recapitulating
behaviors
cells.
Together,
these
results
indicate
crowded
are
defined
overwhelmingly
containing
cytoskeletal
networks.
Chemical & Biomedical Imaging,
Год журнала:
2023,
Номер
1(5), С. 403 - 413
Опубликована: Июнь 28, 2023
Multidimensional
single-molecule
localization
microscopy
(mSMLM)
represents
a
paradigm
shift
in
the
realm
of
super-resolution
techniques.
It
affords
simultaneous
detection
spatial
locations
at
nanoscale
and
functional
information
by
interrogating
emission
properties
switchable
fluorophores.
The
latter
is
finely
tuned
to
report
its
local
environment
through
carefully
manipulated
laser
illumination
strategies.
This
Perspective
highlights
recent
strides
mSMLM
with
focus
on
fluorophore
designs
their
integration
into
imaging
systems.
Particular
interests
are
accomplishments
multiplexed
imaging,
polarity
hydrophobicity
mapping,
orientational
imaging.
Challenges
prospects
also
discussed,
which
include
development
more
vibrant
fluorescent
probes,
optimization
optical
implementation
judiciously
utilize
photon
budget,
advancement
analysis
machine
learning
